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Final Report

For the CBA Back Room Study

By

Douglas A Popken, PhD

Dec 21st , 2005

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Table of Contents

1 Summary.................................................................................................................41.1 Introduction .....................................................................................................41.2 Methodology....................................................................................................4

1.3 Results and Recommendations.........................................................................52 Current (As-Is) Back Room Processes..................................................................6

2.1 Backlist Ordering.............................................................................................72.1.1 Process Flow Diagram..............................................................................72.1.2 Discussion................................................................................................92.1.3 Benchmark Input Data..............................................................................92.1.4 Outputs ..................................................................................................11

2.2 Frontlist Ordering ..........................................................................................122.2.1 Process Flow Diagram............................................................................122.2.2 Discussion..............................................................................................132.2.3 Benchmark Input Data............................................................................13

2.2.4 Outputs ..................................................................................................142.3 Receiving.......................................................................................................17

2.3.1 Process Flow Diagram............................................................................172.3.2 Discussion..............................................................................................192.3.3 Benchmark Input Data............................................................................192.3.4 Outputs ..................................................................................................20

2.4 Returns ..........................................................................................................242.4.1 Process Flow Diagram............................................................................242.4.2 Discussion..............................................................................................252.4.3 Benchmark Input Data............................................................................252.4.4 Outputs ..................................................................................................26

3 Best Practices Review............................................................................................273.1 Inventory Control and Planning .....................................................................273.2 Quality Control.............................................................................................. 293.3 Industry Benchmarks .....................................................................................30

4 Cost Benefit Analysis of Potential Changes ...........................................................314.1 Automated Entry of New Items......................................................................31

4.1.1 Process Benefit Computations................................................................324.1.2 Transition Costs .....................................................................................34

4.2 Decrease Discrepancy Rate ............................................................................344.2.1 Process Benefit Computations................................................................344.2.2 Transition Costs .....................................................................................34

4.3 Decrease Gift Problem Rate ...........................................................................354.3.1 Process Benefit Computations................................................................354.3.2 Transition Costs .....................................................................................36

4.4 Improved/Automated Inventory Control.........................................................364.4.1 Process Benefit Computations................................................................384.4.2 Inventory Related Costs/Benefits............................................................404.4.3 System Cost ...........................................................................................414.4.4 Training Costs........................................................................................42

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4.4.5 Summary................................................................................................424.5 Summary of Cost/Benefit Analysis ................................................................43

5 Discussion and Recommendations .........................................................................445.1 Problems and Discrepancies........................................................................... 445.2 Automating Entry of New Items.....................................................................44

5.3 Improved/Automated Inventory Control.........................................................456 References.............................................................................................................46Appendix A: Summary Store Data From Store Interviews............................................47Appendix B: Store Survey Form...................................................................................49Appendix C: Labor Costs .............................................................................................54Appendix D: Borders Store Technology .......................................................................55Appendix E: Barnes and Noble Store Technology ........................................................56

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1 Summary

1.1 Introduction

CBA initiated the Back Room Study to analyze the ordering and receiving processes ofmember stores. The objectives of the study were to:

a) Identify and analyze the business processes typically used by CBA retailers to fulfillbusiness-to-business transactions with their suppliers.

b) Determine the potential for improving current processes through the introduction ofspecific technologies in the areas of:i) technology standards for ordering, receiving, invoicing, payments, etc.ii) recommended best practices in receiving, invoicing, payments, and returns.iii) standardized information systems such as EDI and web-based transactions

c) Develop metrics and benchmarks for future innovation and improvements

1.2 Methodology

The study included the following components:

a) A retailer interview process allowing for two-way interaction and reality checking. Atotal of nine stores were selected to participate in the interview phase of this study.There were three Large (annual sales > $2M), three Medium (annual salesbetween $750K and $2M), and three Small stores. At each store, a series ofinterviews were performed to determine the key store processes involved withordering and receiving, and the steps occurring within each process. The specificstores visited were selected by CBA. Basic store information is summarized inAppendix A. For complete details see Popken (2005).

b) A broad store survey process to more accurately characterize store processes and fillgaps not covered by the store interviews. To gather data, a web based survey (seeAppendix B) was posted on the CBA website. The response was relatively small (64),but when combined with the interview data was sufficient to characterize storeprocesses to the required degree of accuracy.

c) Simulation modeling to derive quantified results for both as-is and to-be systems.Prototypical simulation models were derived from store interviews and observations,and populated with data from the interviews and survey.

d) Industry research into best practices of retailers with similar product dynamics,including non booksellers.

e) Cost/benefit analysis to prioritize improvement alternatives and determine theirviability when all relevant factors are considered.

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1.3 Results and Recommendations

Four potential improvements were analyzed in detail (Table 1). The estimated annualbenefit to the industry (CBA member stores) is shown in the second column. Each

improvement is mapped to one or more of four implementation recommendations, shownin the third column. The improvement with greatest potential impact isImproved/Automated Inventory Control with estimated annual benefits for the industryof $14,825,539. This was an unexpected finding due both to its relative magnitude and tothe fact that it is more an issue of process control than a process change. Theimplementation recommendation is for a CPFR-based (Collaborative, Planning,Forecasting and Replenishment) solution, which does require a number of changes to thecurrent ordering processes, both frontlist and backlist. The second ranked greatestimprovement is Automating Entry of New Items with estimated annual benefits for theindustry ranging from $838,722 to $2,560,950, depending on how stores currently enternew items into their POS system. The final two improvements analyzed, Decrease Gift

Problem Rate and Decrease Discrepancy Rate, have common recommendations. First,a CBA sponsored supplier certification program would encourage vendors to upgrade thequality of their order-taking and shipping processes. Second individual stores should usea policy of continuous improvement with a focus on reducing errors during ordering andreceiving processes. CBA should continue to sponsor workshops and training on thistopic. See sections 4.2, Summary of Cost/Benefit Analysis and 5, Discussion andRecommendations for further details.

Improvement Estimated IndustryNet Annual Benefit ($/yr)

Recommendation

Improved/AutomatedInventory Control

$14,537,239 Encourage development ofindustry-wide CPFR system

Automating Entry of NewItems

$838,722to$2,560,950

Encourage POS vendors toincorporate neededfunctionality

Decrease Gift Problem Rateby 50%

$546,444 CBA Supplier Certificationprogram;Advocate store-levelcontinuous improvement

Decrease Discrepancy Rateby 50%

$104,653 CBA Supplier Certificationprogram;

Advocate store-levelcontinuous improvement

Table 1. Results and Recommendation Summary

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2 Current (As-Is) Back Room Processes

Through the interview and survey phases of this study, we gathered information todocument the current or as-is backroom processes. There were variations in processes

between stores due to store size, type of POS system, and store policies. It is notnecessary to capture all of these variations to be able to make inferences about the valueof potential system improvements. The idea is to capture only the essential elements ofthe systems in the process models. Towards that end, we developed a benchmarktypical single store process for the Backlist Ordering, Frontlist Ordering, Receiving,and Returns processes. These are then scaled by store size so that results are obtained forSmall, Medium and Large bookstores. The results for each store are thencombined into a single metric using a weighted average approach1. (Note that savingsare proportional to processing volumes so that larger stores have greater benefits.)

The simulation models incorporate labor costs as outlined in Appendix C. Briefly, the

approach applies values from the Bureau of Labor Statistics for the Book, Periodical,and Music Stores (NAICS 451200) industry (BLS, 2004).

The simulation models are populated with data from the study. Much of the data isrepresented as statistical probability distributions. The probability distributions aresampled during the course of the simulation so that each simulation run represents oneof many possible outcomes. Each simulation is run 500 times to generate the distributionof outcomes, characterized by a mean and a standard deviation. The resulting cost dataoutputs represent only the marginal direct labor costs incurred by value added activities fixed costs or idle time costs are not included.

1To compute the weighted average we first convert the outputs to annual values and then weight the result

by the following fractions: Small 75.09%, Medium 21.13%, Large 3.78%. These proportions comefrom current CBA membership data. Note that 4.94% of stores have unknown sales revenue so the storesthat did report are apportioned over the remaining 95.06%.

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2.1 Backlist Ordering

2.1.1 Process Flow Diagram

Generate

Candidate List

Create

Purchase

Order(s)

Order

Mechanism?

EDI

Web Fax

Phone

Review Items

Purchase

Order

Buyer

Backlist

Books and

Bibles

Simulate 1 month of ordering

at a typical store (small, medium,

and large) per simulation run.

Computer

Support

Specialist

Backlist

Music

Backlist

Gifts

Once a week3x per week

Distributor

Once a Day3x per week

No POs this list

Completed List

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Send

Electronic

Purchase

Order

Enter Items

and Place

Order

Print and FaxPhone in the

Order

Immediate

Acknowledg

ment?

File/Post PO's

Process

Secondary

Vendor

Cascade?

No

Yes

Yes

No

Initializer

Initialize Run

Parameters

Figure 1. Backlist Ordering Process

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2.1.2 Discussion

The process is initiated with specific weekly frequencies for each vendor type. Booksand Bibles are ordered up to 3 times/wk, Music is ordered up to 3 times/wk, Gifts are

ordered once a week, while Distributor orders are placed daily (Monday Friday).However, the number of purchase orders generated by each process initiation varieswidely and may even be zero. The model has been calibrated so that the total number ofpurchase orders by vendor type generated in a single month matches the mean andstandard deviation provided by the survey data for that store size. Once the candidate listhas been generated for a given vendor type, it is reviewed by the buyer, with an averagereview time of 10 minutes per purchase order (note the candidate list may incorporate anumber of purchase orders). After the review and possible adjustment, the computednumber of purchase orders is generated. These are transmitted through one of 4 possibletransmission modes. Finally, the purchase orders are filed and posted in the system.

2.1.3 Benchmark Input Data

Parameter Definition (if applicable)

Type Specification Source

BooksPerMonth Number of Book POsin a given samplemonth

ComputedVariable

LogNormal(MeanBooksX,StdDevBooksX) X = Small,Medium, LargeSmall (9.49,16.18)Medium(26.94,26.49)Large (90.59,23.93)

Survey

GiftsPerMonth Number of Gift POsin a given samplemonth

ComputedVariable

LogNormal(MeanGiftsX,StdDevGiftsX) X = Small,Medium, LargeSmall (4.40,6.44)Medium(10.72,11.10)Large(43.34,25.96)

Survey

MusicPerMonth Number of MusicPOs in a given samplemonth

ComputedVariable

LogNormal(MeanMusicX,StdDevMusicX) X = Small,Medium, LargeSmall(7.76,15.46)Medium (12.25,7.18)Large(39.81,13.99)

Survey

DistributorPerMonth Number of DistributorPOs in a given samplemonth

ComputedVariable

LogNormal(MeanDistributorX,StdDevDistributorX) X = Small,Medium, LargeSmall (13.25,16.15)

Medium(22.5,18.96)Large(21.17,20.55)

Survey

Backlist Books andBibles

Order process(multiple POs)

Entity 3 per week (max) Interviews

Backlist Music Order process(multiple POs)


Backlist Gifts Order process(multiple POs)


Distributor Order process Entity 1 per day (max) Interviews

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(multiple POs)Generate Candidate List Activity Time: Exponential(4) min Interviews

Review Items Activity Time = Gamma(NumPO,10)min(sum of exponentials)

Interviews

NumPO Number of POs in a

given order process

Computed

Variable

Poisson(BooksPerMonth/13)

Poisson(MusicPerMonth/13)Poisson(GiftsPerMonth/4.33)Poisson(DistributorPerMonth/21.67)

Interviews

Create PurchaseOrder(s)

Activity Time = Exponential(1) min perpurchase order

Interviews

Order Mechanism? Decisionnode

IF ProductCategory =DISTRIBUTOR THENOrderMechanism = EDIIF ProductCategory =BOOKSANDBIBLES THENOrderMechanism =D4(45,EDI,5,WEB,45,FAX,5,PHONE)IF ProductCategory = MUSICTHENOrderMechanism =D4(45,EDI,5,WEB,45,FAX,5,PHONE)IF ProductCategory = GIFTSTHENOrderMechanism =D4(10,EDI,10,WEB,50,FAX,30,PHONE)

Interviews

Send ElectronicPurchase Order

Activity Time = Exponential(1) min Interviews

Enter Items and Place

Order (web)


Print and Fax Activity Time = Exponential(5) min Interviews

Phone in the Order Activity Time = Exponential(10) min InterviewsProcess SecondaryVendor


ImmediateAcknowledgment

DecisionNode

All distributors and web POs,50% of other electronic POs

Interviews

Cascade? DecisionNode

50% Assumed

File/Post POs Activity Time = Exponential(1) min Interviews

Table 2. Backlist Ordering Input Data

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2.1.4 Outputs

Five hundred simulations (months) were run for each of the three store sizes. Eachsimulation represents 1 calendar month (average of 730 hours).

Entity POs Processed/Month Marginal LaborCost/Month ($)

Mean; Std Dev Mean; Std DevBooks and Bibles POSmall Store 8.93; 11.75 $65.71; $75.58

Medium Store 26.34; 22.91 $235.14; $195.38

Large Store 89.03; 25.31 $1,041.65; $295.39

Music POSmall Store 8.01; 11.88 $48.25; $60.36Medium Store 12.97; 7.86 $96.25; $52.24

Large Store 40.78; 18.47 $359.79; $130.66Gift POSmall Store 4.73; 6.52 $27.46; $35.18

Medium Store 12.08; 11.50 $86.91; $82.17Large Store 47.25; 25.39 $430.39; $246.00

Distributor POSmall Store 13.44; 17.19 $119.08; $134.54

Medium Store 22.50; 20.24 $247.05; $208.51

Large Store 21.03; 21.32 $315.05; $299.27

TotalSmall Store 35.10; 26.08 $260.52; $176.79

Medium Store 73.86; 25.23 $665.37; $317.37Large Store 198.08; 45.69 $2,146.89; $522.35

Weighted Annual Total593.41; 93.22 $5,008.40; $812.89

Average Marginal CostSmall Store $7.42/POMedium Store $9.01/POLarge Store $10.84/POWeighted Average $8.44/PO

Table 3. Backlist Ordering Output Data

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2.2 Frontlist Ordering


Buyer

Frontlist

Books and

Bibles

Prepare

Order(s) with

Vendor

Enter New

Items?

Enter Items

Build PO

Procurement

Clerk

Frontlist

MusicFrontlist

Gifts

Close/File

Purchase

Order

Yes

No

Simulate 1 year of orderingat a typical store (small, medium,


Adjust Open to

Buy

Next

Gift

Order

Next

Music

Order

Next

Book

Order

Initializer

Initialize Run

Parameters

Figure 2. Frontlist Ordering Process

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2.2.2 Discussion

This process is initiated with an order frequency distribution that matches the mean andstandard deviation determined in the survey for each product type: Books (and Bibles),Music, and Gifts. The buyer then spends time with the vendor on the phone or in

person preparing the order. If new items are subsequently entered into the POS system,a procurement clerk spends extra time entering item data. Last, the purchase order isentered into the stores open-to-buy database.


Parameter Definition (ifapplicable)


BooksPerYear # of Frontlist bookorders in a givensample year

ComputedVariable

LogNormal(MeanBooksX,StdDevBooksX) X = Small, Med, LargeSmall(34.15,33.38)Medium(65.17,47.77)Large(147.90,109.98)

Survey

GiftsPerYear # of Frontlist giftorders in a givensample year

ComputedVariable

LogNormal(MeanGiftsX,StdDevGiftsX) X = Small, Med, LargeSmall(18.40,22.32)Medium(52.06,52.61)Large(169.50,149.80)

Survey

MusicPerYear # of Frontlist musicorders in a givensample year

ComputedVariable

LogNormal(MeanMusicX,StdDevMusicX) X = Small, Med, LargeSmall(19.43, 19.98))Medium(26.89,12.02)Large(51.00,28.81)

Survey

Prepare Order(s)with Vendor

Activity IF (ProductCategory = GIFTS) THENTriangular(15,90,360) min

IF (ProductCategory = MUSIC) THENTriangular(10,100,180) minIF (ProductCategory =BOOKSANDBIBLES) THENTriangular(20,110,360) min

Interviews

Enter NewItems?

DecisionNode

Yes if the store enters new items atordering (rather than receiving). IfProductCategory = GIFTS, store mustalso enter gifts into POS

Interviews

Enter ItemsBuild PO

Activity IF (ProductCategory = GIFTS) THENTriangular(15,102,240) minIF (ProductCategory = MUSIC) THENTriangular(30,68,120) min

IF (ProductCategory =BOOKSANDBIBLES) THENTriangular(25,120,240) min

Interviews

Close/FilePurchase Order

Activity 5 minutes Interviews

Adjust Open toBuy

Activity 1 minute Interviews

Table 4. Frontlist Ordering Input Data

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2.2.4 Outputs

Each of 500 simulations represents 1 workyear (2080 hours). For this process, three setsof simulations were run, each representing a different method of handling the way new

items are entered into the POS system.

Entity Orders Processed/Yr Marginal Labor Cost/Yr ($)

Mean; Std Dev Mean; Std DevBooks and Bibles OrderSmall Store 33.39; 33.27 $1,644.61; $1639.95

Medium Store 60.42; 40.64 $3,735.98; $2504.07

Large Store 149.36; 97.03 $12,313.50; $8028.34

Music OrderSmall Store 20.66; 18.27 $506.82; $449.20

Medium Store 28.06; 12.60 $859.73; $388.51Large Store 52.65; 28.30 $2,062.97; $1114.69

Gift OrderSmall Store 18.10; 19.88 $716.61; $786.44

Medium Store 50.67; 40.51 $2,498.92; $2344.50Large Store 158.34; 116.00 $9,964.87; $7311.72

TotalSmall Store 72.16; 42.53 $2,868.04; $1851.50

Medium Store 139.16; 63.59 $7,094.64; $3438.45

Large Store 360.36; 153.84 $24,341.30; $10897.00


Average Marginal CostSmall Store $39.75/OrderMedium Store $50.98/OrderLarge Store $67.55/OrderWeighted Average $47.04/Order

Table 5. Frontlist Ordering - New Items at Ordering, Gifts in System

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Mean; Std Dev Mean; Std DevBooks and Bibles OrderSmall Store 34.38; 29.72 $1,701.46; $1,477.64

Medium Store 67.09; 46.74 $4,142.22; $2,882.93Large Store 137.06; 92.26 $11,324.30; $7,620.79

Music OrderSmall Store 20.67; 18.27 $505.73; $445.59Medium Store 28.05; 12.59 $859.97; $388.27

Large Store 52.66; 28.37 $2,065.37; $1,117.92

Gift OrderSmall Store 18.11; 19.89 $437.08; $478.29Medium Store 50.72; 47.59 $1,588.31; $1,477.35

Large Store 158.57; 117.09 $6,410.46; $4,741.32

Total

Small Store 73.17; 39.58 $2,644.28; $1,600.10Medium Store 145.87; 69.30 $6,590.51; $3,321.47Large Store 348.30; 154.36 $19,800.20; $9,165.58



Table 6. Frontlist Ordering - New Items at Ordering, No Gifts in System

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Mean; Std Dev Mean; Std DevBooks and Bibles OrderSmall Store 33.95; 29.22 $1,124.51; $975.60

Medium Store 67.15; 50.72 $2,842.96; $2,154.14Large Store 143.30; 90.89 $8,357.28; $5,303.67

Music OrderSmall Store 20.67; 18.27 $311.82; $274.22Medium Store 28.08; 12.61 $551.66; $248.25

Large Store 52.69; 28.42 $1,332.38; $725.00

Gift OrderSmall Store 18.89; 20.67 $437.60; $477.95Medium Store 50.73; 47.59 $1,584.92; $1,490.81

Large Store 158.58; 117.01 $6,400.56; $4,734.13

Total

Small Store 72.76; 38.65 $1,873.95; $1,090.12Medium Store 145.96; 69.06 $4,979.54; $2,580.49Large Store 354.58; 146.39 $16,090.20; $6,938.66

Weighted Annual Total98.88; 54.22 $3,067.50; $2,029.60


Table 7. Frontlist Ordering - No New Items at Ordering

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2.3 Receiving


Books

and

Bibles

Shipment

Process Item

(Standard)

Receiving

Clerk

Create Items

Gift

ShipmentMusic

Shipment

Receive

Shipment

Open

Box(es)

Item

Simulate 1 week of receivingat a typical store (small, medium,


Next Gift

Shipment

Next Music

ShipmentNext Book

Shipment

Shipment

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Discrepancy? Trouble Shoot

Shipment

Done

Yes

No

Enter

Item

Enter New

Item?

Yes

No

Problems?

Extra

Processing

Time

Yes

No

Initializer

Initialize Run

Parameters

Item Done

Figure 3. Receiving Process

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2.3.2 Discussion

The receiving process generates shipments by product type with a frequency distributionthat matches the mean and standard deviation determined in the survey for Books (andBibles), Music, and Gifts. The shipment is received into shipping, which usually

amounts to signing the carriers log, moving boxes, and entering data into the store log.When processing begins, the box is first opened and inspected. Contents are thenprocessed one line item at a time (with an average but highly variable rate of 20 lines pershipment). If the line has a problem, extra time (with an average but highly variablerate of 2.5 minutes) is spent resolving the problem. If new items are entered duringreceiving, an average of 1 minute per line is spent to do so. Finally, if there were anyquantity discrepancies in the shipment, time is spent via phone or fax with the vendor toresolve.




BooksPerWeek # of book shipmentsin a given sampleweek

ComputedVariable

LogNormal(MeanBooksX,StdDevBooksX) X = Small,Medium, LargeSmall, Books (10.29,8.23)Medium, Books (33.49,31.38)Large, Books (88.15,69.99)

Survey

GiftsPerWeek # gift shipments in agiven sample week

ComputedVariable

LogNormal(MeanGiftsX,StdDevGiftsX) X = Small, Medium,LargeSmall, Gifts(3.46, 4.35)Medium, Gifts(12.50,11.11)

Large, Gifts(36.70,43.23)

Survey

MusicPerWeek # of musicshipments in a givensample week

ComputedVariable

LogNormal(MeanMusicX,StdDevMusicX) X = Small,Medium, LargeSmall ,Music(3.42, 2.95)Medium ,Music(10.13,7.84)Large ,Music(21.35,20.80)

Survey

Receive Shipment Activity Triangular(1,5,10) minutes InterviewsOpen Boxes Activity Triangular(1,5,10) minutes Interviews

Create Items ControlNode

Exponential(20) lines per shipment Interviews

Process Item(Standard)

Activity IF (ProductCategory = GIFTS)THEN

Exponential (2) minELSEExponential(15) sec

Interviews

Problems? DecisionNode

IF (ProductCategory = GIFTS), useGiftProblemRate, otherwise useNonGiftProblemRate

Interviews

GiftProblemRate % of gift shipmentswith a receivingissue

Parameter 24.53% Survey

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NonGiftProblemRate % of nongiftshipments with areceiving issue

Parameter 5% Interviews

Extra Processing Time Time per line toresolve receivingissue

Activity Exponential(2.5) minutes Interviews

Enter New Items? DecisionNode Items are entered at this point forstores not doing so at ordering. IfProductCategory = GIFTS, theymust also have a policy of enteringgifts into their POS system. % newitems = (average annual frontlistorders x 3 shipments/order) /(average shipments/year) byProductCategory and StoreSize.

Interviews

Enter Item Activity 1 minute per line Interviews

Discrepancy? DecisionNode

GiftDiscrepancyRate,BookDiscrepancyRate,MusicDiscrepancyRate

Interview

GiftDiscrepancyRate Percent of giftshipments with qtydiscrepancyrequiring vendorcontact


BookDiscrepancyRate Percent of bookshipments with qtydiscrepancyrequiring vendorcontact


MusicDiscrepancyRate Percent of musicshipments with qtydiscrepancyrequiring vendor

contact


Trouble Shoot Activity Uniform(5,10) minutes Interviews

Table 8. Receiving Input Data

2.3.4 Outputs

Each of 500 simulations represents 1 work week (40 hours). As with Frontlist Ordering,three sets of simulations were run, each representing a different method of handling theway new items are entered into the POS system.

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Entity Shipments Processed Marginal Labor Cost/Wk ($)

Mean; Std Dev Mean; Std DevBooks/Bibles ShipmentSmall Store 10.62; 7.73 $50.34; $38.03


Music ShipmentSmall Store 4.01; 2.75 $18.79; $15.35Medium Store 10.77; 7.29 $55.83; $38.45

Large Store 19.97; 16.35 $124.71; $105.20

Gift ShipmentSmall Store 3.76; 3.69 $37.36; $39.71Medium Store 12.29; 9.34 $137.28; $107.22

Large Store 28.11; 20.48 $354.43; $237.27

Total

Small Store 18.40; 8.92 $106.50; $57.17Medium Store 55.77; 29.26 $367.34; $177.27Large Store 115.92; 36.14 $897.49; $249.30

Weighted Annual Total1,559.10; 122.81 $9,923.60; $771.40

Average Marginal CostSmall Store $5.79/ShipmentMedium Store $6.59/ShipmentLarge Store $7.74/ShipmentWeighted Average $6.36/Shipment

Table 9. Receiving - New Items at Ordering

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Mean; Std Dev Mean; Std DevBooks/Bibles ShipmentSmall Store 10.26; 6.99 $53.24; $37.57


Music ShipmentSmall Store 4.02; 2.76 $21.90; $17.89Medium Store 10.79; 7.30 $84.33; $59.53

Large Store 19.61; 15.57 $129.72; $104.33

Gift ShipmentSmall Store 3.76; 3.69 $36.71; $40.15Medium Store 12.25; 9.33 $138.08; $107.89

Large Store 27.59 19.73 $346.23; $229.56

Total

Small Store 18.04; 18.49 $111.86; $58.15Medium Store 54.58; 25.59 $400.16; $174.68Large Store 114.57; 36.42 $906.69; $243.13

Weighted Annual Total1,529.30; 152.16 $10,547; $763.87


Table 10. Receiving - New Items at Receiving, No Gifts in System

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Mean; Std Dev Mean; Std DevBooks/Bibles Shipment

Small Store 10.73; 8.97 $55.31; $47.83Medium Store 31.04; 24.72 $173.62; $137.18

Large Store 68.89; 37.80 $437.18; $228.68

Music Order ShipmentSmall Store 4.01; 2.76 $21.21; $16.71


Gift Order ShipmentSmall Store 3.76; 3.72 $38.79; $42.75

Medium Store 12.29; 9.47 $145.60; $113.82

Large Store 26.94; 19.05 $361.28; $236.06

TotalSmall Store 18.51; 10.04 $115.31; $65.06Medium Store 54.12; 26.33 $404.64; $179.05

Large Store 115.18; 36.26 $925.71; $238.08

Weighted Annual Total1,543.80; 118.90 $10,768.00; $793.06


Table 11. Receiving - New Items at Receiving, Gifts in System

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2.4 Returns


Books and

Bibles

Vendor

Generate

Candidate

List

Review

Candidate

List

Pull Items

From Shelf

Markdown

Return

And/Or

Markdown

Process

Pulled Books

Box Shipment

Done

Move to Sale

Area

Manager

Return MarkDown

Music

Vendor

Simulate 1 year of returnsat a typical store (small, medium,


ComputerSupport

Specialist

Stock Clerk

Next

Music

Return

Next

Book

Return

Figure 4. Returns Process

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2.4.2 Discussion

This process is initiated at annual frequencies by product type (Books and Music)consistent with interview data. First, a candidate list is generated from the POS systemby a computer support specialist. The candidate list is then reviewed by a manager to

verify item inclusion and quantities. The adjusted list is used by the stock clerk tophysically pull the items from the shelf and relabel. The majority of the items (85%) arereturned while the rest are marked down. Returns are processed and boxed for shipmentwhile markdowns are relabeled and moved to a sale area.




BookReturnsPerYear Avg. Number ofbook vendors x

returns per vendor

ComputedVariable

Small 52Medium 104

Large Uniform(104,600)

Interviews

MusicReturnsPerYear Avg. Number ofmusic vendors xreturns per vendor

Parameter 12 Interviews

BooksAndBiblesVendor Entity 1 every 2080/Book ReturnsPerYear hours

Interviews

Music Vendor Entity 1 every 2080/MusicReturnsPerYear hours

Interviews

Generate Candidate List Activity Exponential(5) minutes Interviews

Review Candidate List Activity Exponential(22.5) minutes InterviewsPull Items from Shelf Activity IF (ProductCategory = MUSIC)

Triangular(15,34,60) minELSE

Triangular(15,62,240) min

Interviews

Process Pulled Books Activity Uniform (10,15) min per vendor Interviews

Box Shipment Activity Triangular(5,22,60) min per vendor InterviewsMarkdown Activity Uniform(15,30) min per vendor Interviews

Move to Sale Area Activity 5 min per vendor Interviews

Table 12. Returns Input Data

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2.4.4 Outputs

Each of 500 simulations represents 1 work year (2080 hours).

Entity Returns Processed Marginal Labor Cost/Yr ($)Mean; Std Dev Mean; Std Dev

Books and Bibles VendorReturnSmall Store 52; 0 $1,345.32; $57.88Medium Store 104; 0 $3,200.72; $96.35

Large Store 348.82; 139.85 $12,833.00; $5,152.10

Music Vendor ReturnSmall Store 12; 0 $224.29; $21.12Medium Store 12; 0 $270.19; $26.05

Large Store 12; 0 $326.26; $36.41

TotalSmall Store 64; 0 $1,569.61; $61.18

Medium Store 116; 0 $3,470.91; $100.57Large Store 360.82; 139.85 $13,159.30; $5,155.12

Weighted Annual Total86.21; 27.19 $2,409.40; $1,004.70

Average Marginal CostSmall Store $24.53/VendorMedium Store $29.92/VendorLarge Store $36.47/VendorWeighted Average $27.95/Vendor

Table 13. Returns Output Data

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3 Best Practices Review

To help generate process improvement alternatives, we undertook a best practices reviewto understand how the rest of the bookstore and retail industry is handling similar

challenges.

3.1 Inventory Control and Planning

Retailing is all about offering the right product at the right place at the right time for theright price (Fisher, et al., 2000). The technology and the data to do this are currentlyavailable, yet many retailers are not taking advantage of it. Inventory management isconsidered by many Christian bookstores to be largely a right brain function based onexperience and intuition. However, there are ways to combine the art and science.

Borders uses inventory control software called Store Replenishment, developed by

JDA (see Appendix D). The software uses sophisticated forecasting and ordercomputation algorithms to determine the most profitable order plans. The product is saidto consider demand seasonality, order frequency, lead times, vendor minimums, vendordiscounts, and service levels in its computations. Service level refers to the probability ofitems being out of stock.

It is worth noting that Borders software vendor, JDA, is the winner of the 2005 VICSCollaborative Commerce Achievement Award for Best Third Party Provider. TheVICS association honored JDA for its Collaborative Planning, Forecasting andReplenishment (CPFR) solution that enables retailer-manufacturer partners tosynchronize supply chain processes for improved service levels on reduced inventory,

increased sales and more efficient logistics order flow. CPFR is discussed further below.

Barnes and Noble uses an inventory control system developed by i2 technologies, a wellknown developer of optimization based software products (see Appendix E). Theproduct, i2 for Retail , is a suite of modules, including Merchandise Planning. WithinMerchandise Planning is i2 Replenishment Planner, a tool that will reportedly:

Analyze sales data, calculating time-phased forecasts, and creating SKU-levelplans

Calculate dynamic safety stock levels for stores and associated distribution

centers Provide visibility into the inventory commitment at any point in time

Manage Replenishing at both distribution center and store level

Generate plans that reflect actual supply chain capabilities and capacity

Accounting for seasonal and geographical variances in demand

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Both the Barnes and Noble and Borders solutions would not directly apply to situationswith independent book retailers. The large commercial book store chains have their owndistribution system, including distribution centers and centralized buying. However, theconcept of making maximal use of data to make forecasts, compute stock levels, andgenerate orders, is broadly applicable.

A number of retail companies are beginning to use some form of CPFR (CollaborativePlanning Forecasting and Replenishment). The key concept of CPFR is that companiesand their vendors cooperate through information sharing and process integration toimprove the performance of their supply chain interactions to the mutual advantage ofboth parties. A good overview of the subject can be found in the Collaborative PlanningForecasting and Replenishment (CPFR) process model developed by the Voluntary Inter-Industry Commerce Standards (VICS) association (http://www.vics.org/topics/cpfr/cpfr).For a VICS member roster, which includes many well known retailers, seehttp://www.vics.org/about/roster/. The VICS model (VICS, 2004) contains a number ofbusiness scenarios describing various business-to-business supply chain interactions.

The scenario under this standard that applies to the inventory control situation inChristian book stores would be the Store Replenishment Collaboration (VICS, 2005).Below is a brief description;

The theory of store replenishment collaboration is sound. Leveraging the insights of both theretailer and manufacturer [vendor] to drive optimal merchandising and replenishment plans at thestore shelf, trading partners collaborate on retail events and store POS forecasts. The manufacturer[vendor] and retailer then consider other collaboration points that influence replenishment, such as:

- the demand forecast- Store clustering- Store presentation stock/DC safety stock targets- Assortment optimization

- Order sizing- Lead times and distribution methods

The output of Store Replenishment Collaboration is an order or series of orders that are committedover a time horizon. The buyer and seller support order generation with their replenishmentplanning/buying re-buying and production and supply planning organizations respectively. Storecollaboration is focused on the closest link to the consumer and consequently directly influencesshelf availability. The benefits attributed to Store Replenishment Collaboration include greatervisibility to consumer take-away, improved replenishment accuracy, improved in-stocks,overstock reduction, and improved promotional execution. Trading partners have a direct view ofhow consumers are responding to new products, existing shelf distribution and promotional take-away.

The standard contains two models of store interaction: Retailer Managed Release andVendor Managed Release. Under the former, the store generally makes an item forecastand initial order plan, with the vendor then suggesting alternatives or possible exceptions.Under the latter, the vendor generates the initial plan, which is then modified by theretailer.

The standard VICS model is most suitable for large vendors and large customers. Anindividual independent bookstore would not have the resources to establish and maintain
http://www.vics.org/topics/cpfr/cpfrhttp://www.vics.org/about/roster/http://www.vics.org/about/roster/http://www.vics.org/topics/cpfr/cpfr

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such a system on its own. Two viable approaches would be for bookstore POS vendorsto build CPFR into their existing systems, or to use a third-party software acting as anintelligent broker between multiple stores and multiple vendors. A possible starting pointfor the latter is the Above The Treeline software (www.abovethetreeline.com) offeredby the company of the same name. The resulting software would likely need to

incorporate a blend of concepts from the Retailer Managed Release and Vendor ManagedRelease models. The store could possibly use the Vendor Managed Release approach fornew items (frontlist and recent). After an item has been in the stores inventory systemfor several months, it can then be moved to backlist ordering under a Retailer ManagedRelease approach. During these months, the store could generate enough sales data sothat a statistically based forecast using individual store data would provide a valid result.An intriguing possibility is a store-level forecast that combines local store data withaggregated market data. This could essentially provide a unified approach that graduallymoves from a primary reliance on vendor data to a reliance on individual store level dataas that store generates its own sales history data. Note that any forecast must also beaugmented with manual adjustments provided by both store and vendor.

This process would provide a number of significant benefits to the store. First, as thevendor's forecast can be used to set inventory management parameters for new products,less manual buying is needed in the beginning of an item's life cycle. As the forecastsimprove there is less need for manual intervention in backlist ordering as well. Second,as the forecasting accuracy improves, the service level of the store increases. This meansfewer stock-outs. The improved store service is also the greatest benefit from thevendor's point of view, since reduced stock-out rates translate into increased sales. Third,the improved forecasts should also decrease excess inventory, thereby decreasinginventory holding costs, returns cost, and product obsolescence. Last, such an approachmakes it unnecessary for the store to get involved with the details of statistical forecasting.

3.2 Quality Control

Many retailers have difficulty maintaining accurate inventory records (Fisher, et al.,2000). As a result, retailers typically hold more inventory than necessary because of theuncertainties in the true inventory level. One retailer described as very successful anda leader in information systems found that store records were inaccurate on 71% of itsproducts (Raman, 2000). In addition to causing excess inventories, inaccurate inventorycan also lead to lost sales. For example, actual stock-outs may not be detected. Or, itemscan be in the store, but due to customer reshelving and other issues, the items cannot befound. One bookstore chain found that 19% of their stock-outs were due to this

phantom stock-out phenomenon (Raman, 2000). Specific methodologies for inventoryrecord accuracy can be found in CBA (2005d), which advocates that bookstores practicecontinuous improvement.

Continuous improvement is a management philosophy that approaches processimprovement as a continual process of achieving incremental gains (Chase and Aquilano,1995). While technology can play a role in continuous improvement, the techniquesrelate more to organization and management policies and procedures. It also offers sets
http://www.abovethetreeline.com/http://www.abovethetreeline.com/

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of simple tools for diagnosing and addressing problems such as those that lead to thediscrepancies and problems modeled in the Receiving Process above (and analyzedbelow). Tools such as cause-and-effect diagrams and check-sheets can be used toidentify and quantify problem sources, while pareto-analysis can help prioritizeimprovement efforts. These techniques would be applicable to both stores and vendors.

3.3 Industry Benchmarks

The American Booksellers Association (www.bookweb.org) is a not-for-profitorganization representing independently owned bookstores. They perform an annualoperating survey of member stores. The most recent, publicly available survey (ABA,2004) summarizes the results from 197 bookstores at 301 locations representing $451Min annual sales. Comparisons are made to CBA stores via the 2005 Operating StatementSurvey, which summarized results from 132 bookstores.

ABACUS CBA - Operating

Statement SurveyMetricsLow Profit

StoresHigh Profit

StoresAverage

StoreHigh Profit

StoresInventory Turns 2.68 3.37 2.7 2.7

Sales Per Selling Sq.Ft.

$251 $405 $145 $167

COGS % 60.8% 59.6% 61.2% 60.8%Table 14. Selected Metrics from ABA and CBA Store Surveys

Interestingly, the ratio of Cost of Goods Sold to Sales Revenue is relatively flat across allstore types. However, the Sales Per Sq. Ft is strikingly lower at CBA survey stores

versus ABACUS survey stores. We also see that the most profitable ABACUS storesmaintain higher inventory turn rates, 3.37, than CBA or low-profit ABACUS surveystores. The data presents a clear indication that CBA retailers need to improve utilizationof current space and inventory holdings to increase sales and profitability, therebyremaining competitive and viable.
http://www.bookweb.org/http://www.bookweb.org/

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4 Cost Benefit Analysis of Potential Changes

In this section we quantify the benefits of a series of possible process changes made tothe as-is simulation models described in previous sections. In deriving the process

related benefits, we use a computational, rather than a simulation approach. The reasonis that average benefits are readily computed, while the same benefits could be partiallymasked by random statistical variation in the simulation outputs. Average process valuesare taken from the appropriate input data tables. Labor costs are taken from Appendix C.We also examine costs involved in transitioning to the new process such as training orsystem acquisition.

4.1 Automated Entry of New Items

Determining what front list (new stock) items to buy and in what quantity is a very time

consuming process. A single front list order may take anywhere from 30 minutes to sixhours to put together. Over the course of a year, a store may place dozens or evenhundreds of these orders, depending on store size. This process involves working with avendor representative in either the store, at a trade show, or over the phone. In somecases, the representative may use a predetermined package of titles to include in theorder, which along with the precise quantities to order then becomes the subject ofnegotiation. This appears to be a process almost completely dependent on expertjudgment and intuition. In some cases publicly available bestseller lists are consulted asa decision aid.

Entering new items into the POS system is a required but very time consuming next step

in front list ordering. Some stores enter this information immediately after an order isplaced with the vendor, while others do this at the time of receipt. As discussed above,front list orders can be developed with a vendor representative or over the phone. Therepresentative may be filling out an order sheet, or in many cases, entering the orderdirectly into a laptop computer. In all cases, the requested items already exist in thevendors automated system.

A store suggested timesaver would be to have the vendor create an electronic file for thestore containing item data for the order just placed as well as the resulting purchaseorder(s). This file could then be imported into the stores POS system. It appears thatsome vendors may already have item database files that can be imported into POS

systems. This would partially address the automation of entering new item data, but notthe purchase order creation. The store interview portion of this study revealed that thesedatabases are currently little utilized. Possible reasons include: incomplete itemcoverage, new items not in the database at the time of frontlist ordering, avoidance ofsubscription costs, not knowing the functionality exists or how to use it, differences initem classification, or a (mistaken) management decision that self-entry is more efficient.

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4.1.1 Process Benefit Computations

This change would affect Frontlist Ordering or Receiving processes, depending uponwhere the store chooses to enter new items into the system. The level of benefit dependson the store policy for entering new items into the system. Some stores enter new item

data at the time of the frontlist order, and others at receiving. Some enter gift items intotheir POS system, while others do not. We assume that no significant training orequipment costs are involved on the part of the store, only differences in processingcosts/time as detailed below.

New Items at Ordering Gifts In System

The following equation template is used to compute labor savings:

Average Annual Labor Savings = (book orders/yr*(minutes per book order/60 minutesper hour) + music orders/yr*(minutes per music order/60 minutes per hour) + gift

orders/yr*(minutes per gift order/60 minutes per hour))*procurement clerk wage/hr

Average Small Store Savings(34.15*(120/60) + 19.43*(68/60) + 18.40*(102/60))* $7.60 = $924.17

Average Medium Store Savings(65.17*(120/60) + 26.89*(68/60) + 52.06*(102/60))* $8.87 = $2211.44

Average Large Store Savings(147.90*(120/60) + 51.00*(68/60) + 169.50*(102/60))* $11.18 = $7174.77

Weighted Average Savings(.7509)*($924.17) + (.2113)*($2211.44) + (.0378)*($7174.77) = $1432.44/yr

New Items at Ordering No Gifts in System


Average Annual Labor Savings = (book orders/yr*(minutes per book order/60 minutesper hour + music orders/yr*(minutes per music order/60 minutes per hour))*procurementclerk wage/hr

Average Small Store Savings(34.15*(120/60) + 19.43*(68/60))* $7.60 = $686.44

Average Medium Store Savings(65.17*(120/60) + 26.89*(68/60))* $8.87 = $1426.43

Average Large Store Savings(147.90*(120/60) + 51.00*(68/60))* $11.18 = $3953.25

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New Items at Receiving Gifts In System


Average Annual Labor Savings = (book shipments/wk*New Book Rate + musicshipments/wk*New Music Rate + gift shipments/wk*New Gift Rate)*20lines/shipment*(1 minute per line/60 minutes per hour)*receiving clerk wage/hr * 52weeks/yr

Average Small Store Savings(10.29*0.1915 + 3.42*0.3278 + 3.46*0.3068)* 20* (1/60)* $7.16*52 = $515.43

Average Medium Store Savings(33.49*0.1123 + 10.13*0.1531 + 12.50*0.2403)* 20* (1/60)* $8.11*52 = $1168.95

Average Large Store Savings(88.15*0.0968 + 21.35*0.2665 + 36.70*0.2665)* 20* (1/60)* $9.71*52 = $4039.91

Weighted Average Savings(.7509)*($515.43) + (.2113)*($1168.95) + (.0378)*($4039.91) = $786.74

New Items at Receiving No Gifts in System


Average Annual Labor Savings = (book shipments/wk*New Book Rate + musicshipments/wk*New Music Rate)*20 lines/shipment*(1 minute per line/60 minutes perhour)*receiving clerk wage/hr * 52 weeks/yr

Average Small Store Savings(10.29*0.1915 + 3.42*0.3278)* 20* (1/60)* $7.16*52 = $383.69

Average Medium Store Savings(33.49*0.1123 + 10.13*0.1531)* 20* (1/60)* $8.11*52 = $746.70

Average Large Store Savings(88.15*0.0968 + 21.35*0.2665)* 20* (1/60)* $9.71*52 = $2393.78

Weighted Average Savings(.7509)*($383.69) + (.2113)*($746.70) + (.0378)*($2393.78) = $536.38

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4.1.2 Transition Costs

We assume that most of the costs in transitioning to this alternative would initially beborne by POS vendors. This includes software development and any data standardizationcosts. However, the store benefits are large enough that an increase in maintenance and

subscription fees paid by stores to POS vendors could be increased to cover these costs.If annual fees were increased by, say, $100/yr, almost all stores could still benefitsignificantly, regardless of their item entry policy.

4.2 Decrease Discrepancy Rate

Receiving shipments of books and music is a relatively speedy process at all storesvisited. The bottleneck in this process is resolving discrepancies due to either damage orincorrect quantities. All types of shipments suffer from various types of discrepanciessuch as damaged goods, incorrect ship quantities, incorrect prices, or incorrect discount.Typically, about 6% of book and music shipments and 10.5% of gift shipments were

reported to have some sort of discrepancy. A discrepancy is typically resolved with aphone call to the vendor, requiring perhaps 5-10 minutes per incident.


This change would only affect the Receiving Process. In this scenario we assume thatdiscrepancy rates are reduced by half. The following equation template is used tocompute labor savings:

Avg. Annual Labor Savings = 0.5 * (book shipments/wk*book discrepancies/shipment +music shipments/wk*music discrepancies/shipment + gift shipments/wk*gift

discrepancies/shipment)* (7.5 minutes per shipment/60 minutes per hour)*receiving clerkwage/hr * 52 weeks/yr.

Average Small Store Benefit0.5 * (10.29*0.059 + 3.42*0.0599 + 3.46*0.1044) * (7.5/60) * $7.16 * 52 = $27.30/yr

Average Medium Store Benefit0.5 * (33.49*0.059 + 10.13*0.0599 + 12.50*0.1044) * (7.5/60) * $8.11 * 52 = $102.47/yr

Average Large Store Benefit0.5 * (88.15*0.059 + 21.35*0.0599 + 36.70*0.1044) * (7.5/60) * $9.71 * 52 = $325.40/yr



How do we achieve a 50% reduction in quantity discrepancies? In this situation, some ofthe transition costs would be borne by the vendors. Their motivation for doing this could

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be a vendor certification program sponsored by CBA. Vendors would not receive a CBAseal of approval until they had brought the quality of their shipping processes,including accuracy rates, up to a required standard.

Stores would also be responsible for making improvements, most feasibly using a

continuous improvement management strategy. Sources of errors, such as operatorerror during data entry, could be identified, and the potential for future error minimized.For example, if errors occur mostly with fax or phone orders, the store could increaseefforts to convert more ordering to EDI, thus eliminating one or more manual data entrysteps. It is difficult to put a value on the transition costs but we believe it could beaccomplished largely by reprioritizing use of current resources.

4.3 Decrease Gift Problem Rate

Receiving shipments of gift orders is very time consuming relative to books and music.

Some of this, such as unpacking and unwrapping cannot be avoided. Other problems aredue to vendor practices. Problems include:

Individual units of a single item within a box are not labeled separately.

Items may have a stock number but no standard UPC code

Items may have no identifying label at all.

Packing slip is at the bottom of a shipping case.

Packing slip may not reference original purchase order number or invoice number

Invoice is not included with the shipment (sent later).

Packing list may not have individual prices (retail and/or purchase) needed forlabeling

Note that some of these problems apply to non-gift receipts as well, but are much morecommon with gifts. The impact of these problems is that many gift items require time-consuming investigative research during receiving. Some stores try to reduce problemswith gift receipt by not even attempting to capture gifts in the POS system. Of coursethis creates additional problems of its own in trying to manage gift inventory.


This change would also only affect the Receiving process. In this scenario we assumethat gift problem rates (currently averaging 0.2453) are reduced by half. The following

equation template is used to compute labor savings:

Avg. Annual Labor Savings = 0.5 * (0.2453 problems/shipment line) * shipments/wk *20 lines/shipment * (2.5 minutes per line/60 minutes per hour)*receiving clerk wage/hr *52 weeks/yr.

Average Small Store Savings0.5 * (0.2453) * 3.46 * 20 * (2.5/60) * $7.16 * 52 = $131.67/yr

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Average Medium Store Savings0.5 * (0.2453) * 12.50 * 20 * (2.5/60) * $8.11 * 52 = $538.79/yr

Average Large Store Savings

0.5 * (0.2453) * 36.70 * 20 * (2.5/60) * $9.71 * 52 = $1893.98/yr



How do we achieve a 50% reduction in gift problems? In this situation, transition costswould be mostly borne by the vendors. Their motivation for doing so could be a vendorcertification program sponsored by CBA. Vendors would not receive a CBA seal ofapproval until they had brought the quality of their shipping processes up to a required

standard. This is similar to the treatment of overall discrepancy rates.

4.4 Improved/Automated Inventory Control

One of the problems found in current ordering and receiving practices is in the area ofinventory management. The issue here is process control rather than process design.One can have very efficient ordering and receiving processes, but still have poorinventory management. Determining what, when, and how much to order play a majorrole in determining store profitability. It is apparent that bookstore inventorymanagement is as much art as science. Each store visited has seasoned experts doing an

admirable job of inventory management, but with only minimal support from their POSsystems.

All POS systems observed in this study support min/max inventory control parameters,which are used to manage backlist stock. The idea is that when stock levels fall belowthe min, stock should be ordered up to the max (often the two values are the same).This is known as a Reorder Point (ROP) inventory strategy. A candidate restocking orderlist can be generated by the POS system that shows all items whose stock level is belowthe min. Only one of the nine stores interviewed uses the POS computed min/maxvalues from the candidate list directly as given for ordering purposes. In the store survey(Appendix B), only 44% of respondents indicated use of POS min/max values for

ordering purposes. Some stores start with the automatic list, but then manually scrutinizeeach item to either remove it from the list or adjust the quantity. In the store survey, ofthose using min/max values, 36% report that considerable amounts of adjusting arerequired (the rest report some adjustment). Others do not use the min/max at all, butinstead manually scan the entire inventory report on a periodic basis to generate orderlists based on their own judgment and intuition. (The store mentioned above using thecomputed min/max values augments the ordering with this manual technique on asemiannual basis.)

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It should be noted that stores may adjust the suggested maximum due to cash flowconcerns; that is, they may not have the free cash to support the full order(s). But thisalso implies a distrust of the ordering system. If the system were operating correctly,store owners would be more assured that quantities ordered will provide a good return oninvestment (as measured by GMROI) and will find ways to finance the transactions.

Below (Section 4.4.2), we will discuss how an improved system could free up the neededworking capital by reallocation from excess inventories.

Similarly, a candidate returns list can be generated that shows all items whose stock levelis above the max (perhaps with additional demand related screens). Only 25% of storesurvey respondents indicated use of POS min/max values for generating a candidatereturns list. Of these, 60% report that the lists need considerable adjustments. Clearly,there is little confidence in min/max values computed by the existing POS systems.Most users do not even know how they are computed by their system.

At the heart of any inventory control system is a demand forecast. None of the POS

systems supports more than a very rudimentary implied forecasting capability. Theforecast typically consists of taking the last x periods of demand for a single item, andprojecting the average demand from that time span out for another y periods as a demandforecast for that item. For example, assume that next months demand is equal to theaverage for the last two months. The user can set the x and y type parameters, but onlyon a storewide basis. (Users can also usually set the min/max values themselvesmanually on an item-by-item basis.) The result becomes the stocking level (min = max)for that item that appears on a candidate order report or returns list. The POS user has noautomated way of handling demand trends, seasonality, product life-cycle, or mark-down/sale demand. There is also no automated consideration of the relationship betweenindividual item demand and demand for the category in which the item is placed. Thereis no link to vendor demand data that could provide market-wide information.

Another alternative approach used by some POS systems for forecasting/inventorycontrol is the use of strata. Items fall into one of a set of demand stratum such as 1-10units, 11-20, etc. over some fixed time period. All items falling in a stratum are given astock level assigned to that stratum set by the user. This again implies a forecastequivalent to the historical demand.

These deficiencies in the POS systems are likely generating large amounts of hiddencosts to stores inventory holding costs (the costs of having too much of something) andlost sales costs (the costs of not having enough of something).

We propose an alternative CPFR based system as discussed in Inventory Planning andControl in the Best Practices Review section. This system does not currently exist forindependent bookstores, but could be developed by modifying existing POS or third-party inventory planning software. The system would be integrated with the store POSsystem so that real time inventory and sales data could be utilized. The system wouldprovide item level forecasts for both frontlist and backlist items using both store andvendor data. Statistical forecasts would be augmented by adjustments from store and

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vendor personnel providing judgment-based information. The forecasts would then beused by algorithms that compute optimal stocking levels for a given desired service level.These stock levels would provide the basis for ordering decisions and returns decisions.The process would be automated so that complete order and return lists could begenerated as needed. Virtually all POS systems already possess the capability to generate

purchase orders from candidate order lists. Clearly, a CPFR system would require someform of vendor accountability, such as GMROI metrics2 (by vendor, category, etc) onfrontlist items whose order quantities are derived from vendor forecasts. There is nosingle definition of GMROI. For example, there are different ways to compute theaverage holding cost component


This alternative has the most pervasive effects, with impacts on Backlist Ordering,Frontlist Ordering, and Returns processes.

Backlist Ordering

In this alternative, we assume that the average time needed to review the candidate buylist is reduced from 10 minutes/vendor (purchase order) to 5 minutes/vendor. No furtherreduction can be assumed because the store still must spend time making adjustments forunusual circumstances that would not be known via statistical forecasting alone. Theseinclude in-store promotions/sales or other local events that might boost or drop sales forparticular titles. The following equation template is used to compute labor savings:

Avg. Annual Labor Savings = 0.5* (10 minutes per order/60 minutes per hour)*(bookorders/month*book buyer wage/hr + music orders/month*music buyer wage/hr + gift

orders/month*gift buyer wage/hr + distributor orders/month*book buyer wage/hr)*12months/yr

Average Small Store Savings(5/60)*(9.49*$12.05 + 7.76*$9.27 + 7.76*$9.27 + 13.25*$12.05)*12 = $417.89/yr

Average Medium Store Savings(5/60)*(26.94*$15.54 + 12.25*$12.05 + 12.25*$12.05 + 22.50*$15.54)*12 = $1063.52/yr

Average Large Store Savings(5/60)*(90.59*$21.27 + 39.81*$15.54 + 39.81*$15.54 + 21.17*$21.27)*12 = $3614.43/yr


2There is no single definition of GMROI, for example, there are different ways to compute the average

holding cost component. Therefore any CPFR system will need to account for and reconcile these existingformulations before introducing a standard.

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Frontlist Ordering

Under this alternative, we assume that the time spent working with book and musicvendors to prepare an order is cut by 25% (we assume that gifts remain intrinsicallydifficult). With better information, both selection of titles and determination of order

quantities are simplified. First we note that the current order preparation times have thefollowing mean values, based on the formula for the mean of triangular distributions:

Books and Bibles: 1/3 *(20 + 110 + 360) = 163.33 minutesMusic: 1/3*(10 + 100 + 180) = 96.67 minutesGifts: 1/3*(15 + 90 + 360) = 155 minutes


Avg. Annual Labor Savings = 0.25* (book orders/yr*(minutes per order/minutes perhour)*book buyer wage/hr + music orders/yr*(minutes per order/minutes per

hour)*music buyer wage/hr + gift orders/yr*(minutes per order/minutes per hour)*giftbuyer wage/hr)

Average Small Store Savings0.25* (34.15*(163.33/60)*$12.05 + 19.43*(96.67/60)* $9.27 + 18.40*(155/60)* $9.27) = $462.76/yr

Average Medium Store Savings0.25* (65.17*(163.33/60)*$15.54 + 26.89*(96.67/60)* $12.05 + 52.06*(155/60)* $12.05) = $1224.87/yr

Average Large Store Savings0.25* (147.90*(163.33/60)*$21.27 + 51.00*(96.67/60)* $15.54 + 169.50*(155/60)* $15.54) = $4161.24/yr


Returns

Under this alternative, we assume that the frequency of returns is decreased in proportionto the reduction in average inventory (see Cost/Benefit analysis below). This implies areduction of 33,357/159602 = 20.9%. The following equation template is used tocompute labor savings:

Avg. Annual Labor Savings = Current labor cost/yr * .209

Average Small Store Savings1345.32*.209 = $281.17/yr

Average Medium Store Savings3200.72*.209 = $668.96/yr

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Average Large Store Savings12833*.209 = $2682.09/yr


4.4.2 Inventory Related Costs/Benefits

We assume that one outcome of improved forecasting and inventory control is a moreefficient deployment of inventory resources, that is, an improved ability to stock the rightitem, at the right place, at the right time. This will come about from a reduction ofaverage stock in some items, and increases of average stock in others. In our analysis, wewill also assume no net change in inventory investment dollars. To estimate the netbenefits to an average CBA store, we begin by noting the following values from the mostrecent CBA Operating Statement Survey (CBA, 2005a):

Averages Sales Per Store = $695,172Average Cost of Goods Sold = .612 x SalesAverage GMROI = $1.69

The formula for GMROI (Gross Margin Return on Inventory) is:

GMROI = (Sales - Cost of Goods Sold) Average Inventory

If we assume an average store is one in which all operating values are at the averagevalues in the survey, we can say that:

GMROI= ($695,172 - $425,444) / Average Inventory = $1.69

If we solve for Average Inventory, we find:

Average Inventory = $159,602

As a double-check, observe that the corresponding inventory turnover is

Cost of Sales/Average Inventory = $425,444 / $159,602 = 2.67

This value is consistent with the 2.7 average turnover reported in the same survey. At

this point we need to make an assumption about an ideal inventory turnover. A higherinventory turnover is not always better; too high and you start getting too many lost salesfrom lack of inventory. We shall assume that the average value, 3.37, for high profitstores from the ABACUS survey above reflects a value close to this ideal. This higherinventory turnover indicates lower average inventory for the same cost of sales:

Average Inventory = Cost of Sales/Inventory Turnover = $425,444 / 3.37 = $126,245

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implying an average inventory reduction of $159,602 $126,245 = $33,357. Thisequates to investment that can be redeployed by the store to increase sales (decrease lostsales). It is reasonable to assume that this amount of inventory investment can beredirected profitably. For example, respondents to the out-of-stock question in the CBACore Inventory Survey reported an average out-of-stock rate of approximately 29%

(CBA, 2005b). A CBA affiliated Christian marketing group reported an average out-of-stock rate (of their suggested core inventory) of 43% among a large sample ofindependent stores (CBA, 2005c). (Note that these out-of-stock values represent twocomponents: a) not carrying recommended titles, and b) not maintaining stock of titlesthat are carried. The store survey showed that the level of the second component, (b),averages around 13.63%.) Based on this data, we will assume that the average of $33,357can indeed be redeployed so that it generates at least the same ratio of annual sales toaverage inventory as the remaining stock; that is:

Average Additional Sales = ($695,172 / $126,245) x $33,357 = $183,681 .

We then assume that the average after tax profit rate (for profitable stores in CBA, 2005a)of 3.7% would provide

Average Net Profit = .037 x $183,681 = $6,796/yr

This would be the profit in an average store with $695,172 in annual sales (as defined in theOperating Statement Survey). We can use an approximation technique to scale this value forstore size. Small stores are defined as those with 0 to $750K in annual sales revenue. If we usethe midpoint of $375K as an average for small stores, we can estimate the Small store AverageNet Profit as (375,000/695,172)*6,796 = $3,666. Medium stores are defined as those with salesbetween $750K and $2M. Using the midpoint of $1,375,000 we can estimate the Medium storeAverage Net Profit as (1,375,000/695,172)*6,796 = $13,442. To estimate the Large store average,

we use the fact that 695,172 should be a weighted average of the three store size specific values.Using our weightings from the CBA membership survey we have (.7509)*3666 + (.2113)*13442+ (.0378)*X = 6796, where X is the desired quantity. Solving for X we determine that the Largestore Average Net Profit = $31,823.

Average Net Profit (Small Store) = $3,666/yrAverage Net Profit (Medium Store) = $13,442/yrAverage Net Profit (Large Store) = $31,823/yr

4.4.3 System Cost

In this section we estimate the marginal installation and subscription costs to a softwaresystem implementing the proposed CPFR capabilities. To overcome possible user trustissues, we assume a third-party vendor, not directly affiliated with the current POSvendors - a model exemplified by Above the Treeline. The relevant costs are themarginal costs of including these additional inventory control capabilities into theexisting software. Currently, Above the Treeline has a one-time installation costaveraging $500, and a monthly subscription rate averaging $50-$100 (Steele, 2005). Asa worst-case estimate, it is safe to assume that the marginal costs would be no greater

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than the existing costs, that is, the total future costs must be less than double the currentcosts. This implies marginal costs no greater than $500 at installation and $75/month($900/yr) on average for the subscription.

4.4.4 Training Costs

Most POS vendors as well as Above the Treeline offer free or low cost training for theirproducts. The largest training cost to stores is the time spent by users. As with SystemCosts above, we seek to estimate the marginal training costs associated with theproposed CPFR capabilities. The basic training needed to use the current Above theTreeline system is approximately 2 hours (Steele, 2005). However, it would likelyrequire an additional 4 hours to gain familiarization and apply it to the specific store(CBA, 2005e). We shall use these as our benchmark, and assume that the marginaladditional training time for CPFR capabilities is 6 hours. We further assume that eachstore will need to train three people (manager, frontlist buyer, and backlist buyer). Wealso assume that this training occurs yearly due to personnel turnover and new software

developments. If the training occurs online or at existing CBA conferences then noadditional travel costs are involved, implying 18 hours annual training time. Accordingto the CBA survey (CBA, 2005a) the average salary for non-store owner managers is$26,000/yr or $12.50/hr.

18 x $12.50 = $225/year

4.4.5 Summary

Using the following computation template:

Net Benefit = Process Benefits + Inventory Related Benefits System Cost Training

Average Small Store Savings($417.89 + $462.76 + $281.17) + $3,666 - $900 - $225 = $3,702.82

Average Medium Store Savings($1,063.52 + $1,224.87 + $668.96) + $13,442 - $900 - $225 = $15,274.35

Average Large Store Savings($3,614.43 + $4,161.24 + $2,682.09) + $31,823 - $900 - $225 = $41,155.76

Average Net Benefit =

($675.14 + $763.60 + $453.86 ) + $6,796 - $900 - $225 = $7,563.60/yr

There is also a one time installation cost of$500.

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4.5 Summary of Cost/Benefit Analysis

The four improvement options are summarized in the table below in order of estimated benefit.

Individual Store Values

Net Annual BenefitsOption

TransitionCost Small

Store($/yr)

MediumStore($/yr)

LargeStore($/yr)

AverageStore($/yr)b

Industry

Net AnnualBenefit($/yr)c

Improved/AutomatedInventory Controla

$500 (onetime)

3,702.82 15,274.35 41,155.76 7,563.60 $14,537,239d

Automating Entry ofNew Items

$100/yr 283.69to824.17

646.70to2,111.44

2293.78to7,074.77

436.38to1,332.44

$838,722to$2,560,950

Decrease GiftProblem Rate by50%

Storemanagementrefocus

131.67 538.79 1,893.98 284.31 $546,444

DecreaseDiscrepancy Rate by

50%

Storemanagement

refocus

27.30 102.47 325.40 54.45 $104,653

Table 15. Cost/Benefit Summary

Notes:a) Assumes 50% reduction in backlist ordering time, 25% reduction in frontlist ordering time, 20.9% reduction in returns processed,and $975/yr subscription and training costs.b) Uses the weights derived from the CBA membership survey: Small 75.09%, Medium 21.13%, Large 3.78% (see footnote 1)c) Assumes 1,922 stores per the CBA membership survey, i.e., Average Store [value] x 1922 storesd) Does not include the assumed one-time cost of $500 x 1922 stores = $961,000

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5 Discussion and Recommendations

5.1 Problems and Discrepancies

We have seen that reduction of error rates during receiving (both problems anddiscrepancies) has the least impact in terms of directly measurable marginal labor costs.This is not to say that quality improvement efforts here should not be pursued. For one,the costs computed here are likely to underestimate the total true costs. Errors introducedinto the system typically have secondary ripple effects such as the costs of inaccurateinventory records. In addition, correcting problems with incoming shipments is a highlyvisible irritant to those dealing with them, as it is correctly perceived as wasted andunproductive time. Wages paid at Christian stores tend to be below market value atradeoff gladly made by employees thus some of the costs of wasted time are bornedirectly by the employees and thus not measured. There are several recommendationsrelated to these areas. First, CBA should initiate a supplier certification program.

Bookstores would have access to supplier ratings, and would find it in their interest to dobusiness with certified vendors. Vendors would then find it in their interest to improvethe quality of their order taking and shipment processes. There would likely be a greatermove to EDI by both parties as it would automate more activities now subject to manualdata entry and subsequent human error.

There has been some discussion of a supplier certification program by the ChristianProduct Standards Group (CPSG, 2004). This discussion has focused on EDI standardsand electronic submission of product data (the latter could actually benefit theAutomating Entry of New Items issue discussed below). These standards should be acomponent of supplier certification, but separate quality-based criteria are also needed.

These would necessitate some form of sampling/data collection to monitor vendorcompliance. There are numerous sources of information on designing a suppliercertification program. See, for example, Maass (1990), or seminars provided by theAmerican Management Association (http://www.amanet.org/seminars/).

The second recommendation is that individual stores pursue policies of continuousimprovement with regard to ordering and receiving processes. The focus needs to be oneliminating possible sources of error automating manual processes; careful selection ofvendors, considering not just price but quality, reliability, and timeliness (or whether theyare certified as suggested above); more inventory cycle counting; and error diagnosisand tracking tools such as cause-and-effect diagrams and pareto-analysis.

5.2 Automating Entry of New Items

The process change with greatest impact is automating the entry of new (frontlist)items. The impact is great because, first, item entry is time-consuming, whether done atreceiving or ordering. Second, new item entry is done frequently. During the storeinterview phase of this study, several wondered why this information could not be
http://www.amanet.org/seminars/http://www.amanet.org/seminars/

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provided by most vendors in electronic format at the time the order was placed, and thenimported directly into their POS system. This study recommends that CBA encouragesuppliers to provide this data as a standard part of frontlist ordering, and encourage POSvendors to provide a simple import capability for the data. Suppliers could perhaps bemotivated by making this a part of supplier certification. POS vendors could be

motivated by a justified increase in system maintenance and subscription prices paid bystores (we assume $100/yr in the analysis).

5.3 Improved/Automated Inventory Control

The change with overall greatest impact, improved/automated inventory control, is less aprocess change than a process control change. It is arguably the most difficult to achieveas it relies on a hypothesized information system that does not currently exist. Theclosest current product is the web-based Above The Treeline software. It has thecapability to integrate with most store POS systems and already has access to sales datafrom a number of vendors. It lacks a full blown CPFR capability as well as the ability to

make sophisticated forecasts from historical data. This study recommends that softwarevendors be encouraged to develop their products in this direction as the potential benefitsare relatively large. This is nota recommendation for Vendor Managed Inventory (VMI),but a middle ground incorporating a combination of Retailer Managed Release andVendor Managed Release as described earlier in this report (see section 3.1). It providesthe best way to compete with the sophisticated and proprietary inventory control systemsused by major chain bookstores. Clearly this will require careful consideration of storerequirements and preferences. Processes must be developed whereby stores and vendorscooperate to develop joint forecasts and collaborate to develop purchase orders. Thesystem will require accountability metrics, such as GMROI by vendor, category, etc, sothat stores will be willing to give up a certain amount of control. In return they would

enjoy reduced inventory costs (holding and lost sales), more efficient ordering, andreduced returns.

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6 References

ABA. 2004. Results from the New ABACUS [2003]. American Booksellers Association,http://www.bookweb.org/graphics/pdfs/ABACUS2003presentation.pdf

BLS. 2004. Bureau of Labor Statistics Hourly Wage Data (National Aggregate). From NAICS451200 - Book, Periodical, and Music Stores. (May 2004) -(http://www.bls.gov/oes/current/naics4_451200.htm#b11-0000 )

CBA. 2005a. Operating Statement Survey. CBA.

CBA. 2005b. Core Inventory Survey. CBA.

CBA. 2005c. Personal communication from Eric Grimm, CBA, 9/29/2005.

CBA. 2005d. Best Practices to Keep the Numbers Right. Retail Application Lab given at the

International Christian Retail Show 2005, Denver, CO, July 2005( http://www.trinitygroup.biz/presentations.htm )

CBA. 2005e. Personal communication with Mike Hockett, CBA, 12/13/2005

Chase, RB and NJ Aquilano. 1995. Production and Operations Management Manufacturingand Services. Irwin, Chicago.

CPSG. 2004. Christian Retail EDI Certification Strategy Recommending an E-CommerceSupply Chain Strategy for Today and Tomorrow, Presentation by the Christian Product StandardGroup, April 30th, 2004. (http://www.cpsg.org/docs/docs_finished/certif_ppt.ppt )

Fisher, ML, A Raman, and AS McClelland. 2000. Rocket Science Retailing is Almost Here Are You Ready? Harvard Business Review, Jul-Aug 2000, 115-124.

Maas, R, JO Brown, and J Bossert. 1990. Supplier Certification, a Continuous ImprovementStrategy. Quality Press.

Popken, DA. 2005. Interim Report for the CBA Back Room Study. Report presented to CBAby Systems View, 9/27/2005.

Raman, A. 2000. Retail-data quality: evidence, causes, costs, and fixes. Technology in Society,22(1), 97-109.

Steele, R. 2005. Personal communication with Bob Steele, Above the Tr

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