Index

A

Achabal, D. D., 23, 271, 275, 276, 278,281, 289

Active decisions, 198Actual inventory, 54

and inventory record, discrepancybetween, 65

Adams, P. W., 178Adelman, D., 227Adjusted inventory turnover (AIT), 29,

36–37Advertising, influence on customer

decisions, 12Aggregate demand model, 157Aggregate echelon inventory positions, 214Aggregate level inventory management in

retailing, directions for futureresearch on, 50

Agrawal, N., 1–8, 11–23, 67, 112, 117–118,128, 148, 160–161, 169, 184, 193,207–231

Agrawal and Smith (2003), modeling andsolution approach in, 128

Ahire, S. L., 216Akella, R., 124, 160Albert Heijn (Dutch supermarket chain),

132, 133assortment planning in practice, 142–144price and promotion at, 134replenishment system at, 119

Alliance, information sharing, 84, 85, 86Allocation decision, 213, 214Allocation policies

types of, 217used at warehouse, 212

Alptekinoglu, A., 103Anderson, E., 156Anderson, P., 60Anderson, S. P., 102, 109, 110, 113

Andrews, R. L., 188Anily, S., 227Antitrust

concerns, and category captainship,93–94

issue, 82Anupindi, R., 5, 124, 135–136, 155, 160,

219, 224, 225Archibald, T. W., 223Argote, L., 61Arrow Electronics, high inventory record

accuracy in, mechanism for, 59Arrow, K., 162Assets, of retail firm, 25Assortment

and inventory planning, 4–6importance in product availability, 53

key marketing objectives for, 12optimization

heterogeneity in, 201ornamentation, 12problem, solution technique for, 173–174reductions, 159, 160and stocking problem, 165–171

basic formulation, 165–168modeling no purchase, 168–169optimization model and some special

cases, 170–171reformulation, 169–170

strategy, based on percent sales vs. salesvelocity matrix, 142

Assortment-based substitution, 107,117, 192

estimation of, 136–137in exogenous demand model, 113

Assortment customization, at BordersBooks, 146

Assortment decision, 156seasonality of, 187

309

Assortment planning, 99at Albert Heijn (BV), 143–144at Best Buy, 137–139at Borders Group Ink, 139–140constraints on, 13consumer search in, 117decentralized approach to, 130in decentralized supply chains, 125demand estimation

of MNL, 130–135of substitution rates in exogenous

demand models, 135–137demand models, 106

consumer driven substitution,107–108

exogenous demand model, 110–113locational choice model, 113multinomial Logit (MNL) model,

108–110used in, 5

directions for future research, 147–149dynamic, 126–127under exogenous demand models, 117

Kok and Fisher model, 119–123Smith and Agrawal model, 118–119

factors for consideration, 5industry approach to

Albert Heijn (BV), 143–144Best Buy, 137–139Borders, 139–140Tanishq, 140–142compared to academic, 144–146

and inventory management, additionalaspects of, 22

under locational choice, 123–124with MNL: The van Ryzin and Mahajan

model, 114–117models

for retailers of multi-featuredproducts, see Retail assortments,optimization for diverse customerpreferences

for tractability, assumption madein, 112

models with multiple categories,127–130

in multi-store, 148in practice, see Assortment planning,

industry approach torelated literature

multi-item inventory models, 103–104product variety and product line

design, 102–103

shelf space allocationmodels, 104–106variety, perception of, 106

and replenishment, attribute-focused, 146retailers’ dependence on manufacturers

for recommendations on, 92at Tanishq, 140–142See also Assortment selection, and

inventory planningAssortments

rationalization using household scannerpanel data, see Product variety, onretail shelf, management of

Assortment selectionand inventory planning

in decentralized supply chains, 125dynamic, 126–127under exogenous demand models,

117–123under locational choice, 123–124with MNL: van Ryzin and Mahajan

model, 114–117models with multiple categories,

127–130optimal, 90and presentation design decisions,

relationship between, 13Assortment selection decision, 92

delegation of, 90–93Assortments optimization, for diverse

customer preferences, 183DVD player data base, illustrative

application for, 198comparing model’s predictions to

retailer’s sales data, 199impact of customer preference

structure, 200–203optimal assortment vs. expected

revenue of retailer’sassortment, 200

model description, 185modeling consumer’s purchase

decision, 186–191optimal assortment, properties of,

195–197optimization problem, solving of,

197–198retailer’s assortment optimization,

191–195Atali, A., 66Atkins, D. P., 227Automated replenishment systems, 20, 54

failure of, 63Aviv, W., 293

310 Index

Aviv, Y., 221Avsar, Z. M., 104Axsater, S., 6, 207–208, 217, 222, 230, 231Aydin, G., 7, 125, 147, 148, 237, 243

B

Backlogging assumption, 65Backordering, of unmet demand, 211, 216,

218, 221, 231Backrooms, 68, 119Baker, K. R., 228Balakrishnan, R., 29Balance assumption, 214, 217, 218Balintfy, J., 227Banana Republic, 13Bankruptcy, 35

effects on model estimation, 50Barlow, R. E., 244Base stock policy, 215, 218

adjusted, 65Basket profits, 130Basket shopping consumers, 128, 129Bass, F., 279Bassok, Y., 104, 124, 160, 219, 224, 225Basuroy, S., 109Batch ordering, 218Baumol, W. J., 128Bawa, K., 164Bayers, C., 54Bayesian learning mechanism, use in

retail, 127Baykal-Gursoy, M., 104Belgian supermarket chains, Corstjens

and Doyle model for shelf spaceallocation in, 105

Bell, D. R., 128, 242Belobaba, P. P., 293Ben-Akiva, 109, 110, 129, 187, 189, 190Benchmarking, 3, 49

of inventory productivity of retailfirms, 26

Bergman, R. P., 54Bertrand, L. P., 223Besanko, 274Best Buy (electronics retailer)

assortment planning in practice, 50,137–139

rebate handliing at, 256Bharadwaj, S., 157Billesbach, T. J., 29Bish, E. K., 117Bitran, R., 273, 274, 275, 293Bluedorn, A., 61

Boatwright, P., 102, 156, 159, 160, 164Bolton, R. N., 275Bookbinder, J. H., 223Borders Group Inc. (book and music

retailer)assortment planning in practice, 139–140average employee turnover in, 61drivers of misplaced products in, research

analysis on, 71–73effect of product variety and inventory

levels on store sales in, 73–75misplaced products in, 56reduced profits due to misplaced

products in, 53Borin, N., 105, 158, 159Borle, S., 159, 160, 164Bottom-up analysis, 16Braden, 274Bradford, J. W., 210Bramel, J. D., 227Brand(s)

choice, 110of first and second preference, cross

classification of, 173management, by vendors, 5noncaptain, 125substitution between, asymmetries in

patterns of, 173See also Category captainship

Brand strength, 83Broniarczyk, S. M., 106, 159, 160Brooks, R. B., 54Brown, K. A., 54Bucklin, R. E., 131, 190Bulkeley, W. M., 238, 239, 256Bull whip effect, 220Bultez, A., 105, 158Butz, D. A, 242

C

Cachon, G. P., 7, 91, 110, 116, 117, 128, 129,145, 147, 148, 184, 218, 220, 227

Caldency, R., 293Caldenty, R., 273–274Camdereli, A. Z., 66Cameras, traditional and digital, historical

sales of, 137, 138Campo, K., 108, 136Capital intensity, 26, 27

correlation with inventory turnover, 29Capital intensity (CI), 36, 37Carpenter, G., 157Carrefour, 80

Index 311

Cashback allowances, consumer rebate inautomotive industry, 243

Cassidy, A., 54Catalog channel, 14, 15

customers from, shipment ofmerchandise to, 20

Catalogs, as advertising mechanism, 22Category captain

selection, points for, 88use of, 80

Category captainship, 125adverse effect of, 94benefits of, 87, 88relationships, basis for, 96

Category captainship, in retail industry, 79categories of existing research on, 4,

82–83antitrust concerns, 93–94delegation of assortment selection

decision, 90–93delegation of pricing decisions, 86–90emergence of, 84–86

future research directions, 95–97impact of, 94–95implementations in practice, 80–82

Category management (CM)decentralized assortment control in,

129, 130ECR component, 156See also Category captainship; Product

variety, on retail shelf,management of

Cattin, P., 198CGS (cost of goods sold)sit, components of,

31, 32Chang, D., 29Chang, P. L., 220Channel coordination, 66Channel rebates, see Retailer rebatesChen, F., 103, 215, 216, 221, 231Cheng, 275Chen, H., 28–29Chen, K. D., 185Chen, M. S., 220Chen, X., 243Chiang, J., 131Children furnishings, concept of brand retail

marketing, 11Chintagunta, P. K., 131, 133, 165, 190Choi, S. C., 83–84Chong, J-K., 131, 161, 184Christopher, M., 108Circa 1990, 1

Clark, A. J., 215Clearance markdown

algorithms, 289management, components of, 273pricing system, 272

Clearance, and markdown optimization, 21Clearance pricing in retail chains

discrete price changes, 281power function form, solution for,

282–285mathematical models for, 273model specifications and optimality

conditions, 276model formulation, 277–281

numerical examples, 285–289related research, 273–276trends in, 271–273

Cohen, M. A., 222–223, 228Colgate, as category captain in oral care

category, 80Collier, D. A., 228Competition

duopoly, 130markdown competition, 293, 298–303

managerial implications, 303–305model, 295–296monopolistic retailer, 296–298

Competitive collusion, 93Competitive exclusion, 89, 90, 93, 96Computing technology, application in retail

business, 1, 2Consideration sets

effect on expected profit, 202use of, 201–203

Constant-split propertyconsequence of, 250rationale behind, 251

Consumer rebate, 238, 240modeling, 239pull price promotions, 241in supply chain, 249–252

together with retailer rebate,243–247

use of, 241, 247Consumer(s)

basket shopping, 128behavior, basket effect of, 148choice, 106, 161education, 92heterogeneity of, 171model, for study of product variety

management on retail shelf,162–165

312 Index

package goods, purchasing behaviorfor, 190

purchase behavior model, basis of, 133purchase decision, modeling, 186–191substitution behavior of, 157, 160substitution, consumer-driven, 107–108substitution between, heterogeneity in

patterns of, 173Consumer segments, 147

discrimination between, 148Containers, 19

integer number of, 22Continuous review inventory systems,

229–231Conwood, antitrust row with UST, 82Cookware essentials, concept of brand retail

marketing, 11Cooper, L. G., 131, 134Cornuejols, G., 169, 170Corstjens and Doyle model, for shelf space

allocation, 105Corstjens, J., 95Corstjens, M., 95, 105, 143, 158Cortsen, D. S., 157Costs

holding and penalty costs, 213, 220inter-node transportation, 220inventory costs, 242reductions, opportunities for, 21

Coughlan, A. T., 156CPFR (collaborative planning,

forecasting and replenishment)programs, 69

Cross-channel optimization, in supply chainplanning, 22, 23

Cross channel pricing tradeoffs, 23Cross-docking policy, 212Cross-price sensitivity, 83, 88CRSP (Center for research in security prices)

database, 28Current priority allocation (CPA), 217Customer preferences, optimization of

assortments for, 183DVD player data base, illustrative

application for, 198comparing model’s predictions to

retailer’s sales data, 199impact of customer preference

structure, 200–203optimal assortment vs. expected

revenue of retailer’sassortment, 200

model description, 185

modeling consumer’s purchasedecision, 186–191

optimal assortment, properties of,195–197

optimization problem, solving of,197–198

retailer’s assortment optimization,191–195

Customer(s)choice behavior of, assumptions

characterizing, 111clustering of, 204heterogeneity, 201

impact on optimal assortments andexpected profits, 204

from internet and catalog channels,shipment of merchandise to, 20

misplacement of products by, 58‘‘no purchase’’ option of, reasons for, 186retention, 157, 159store sales vs. customer entrances, 64substitution, patterns with respect to, 100variety-seeking behavior, factors

promoting, 106Cycle stock, 38

D

Dada, M., 96, 243Dalton, D., 61Daniels, R. L., 178Dayton Hudson, 13DCs, see Distribution centers (DCs)Dealer incentives, retailer rebate in

automotive industry, 243Decision making process, retailer’s, 23Decisions, tactical and strategic, impact of

execution problems on, 65See also Specific types

Deep discount drug stores, growth of, 156De Groote, X., 103, 123DeHoratius, N., 3–4, 53, 55, 58, 60, 62, 63,

64, 65–66, 68, 69, 136De Kok, A. G., 63, 184, 192–193,

216, 227De-listing of firms, 35Demand

estimation, improvement of, 67forecasting, 21modeling of, 209–211non-stationary, 215pooling, effects on product variety, 26uncertainty, incorporation of, 178See also Unmet demand

Index 313

Demand models, 106consumer driven substitution,

107–108exogenous, 110–113

assortment planning under,117–123

demand estimation of substitutionrates in, 135–137

locational choice model, 113MNL model, 108–110

Demeester, L., 29Demoralization, of employees, 61Dempster, A. P., 132Denend. L., 20Depot effect, 209Desai, P., 103Desrochers, D. M., 82, 93, 94Dhar, S. K., 145Dhebar, A., 274, 279Diks, E. B., 216, 227Diseconomies of scale, 124Display effect (effect of facings) on sales, 178Distribution centers (DCs)

execution problems in, 54merchandise handling capabilities at, 14shipments from DCs to stores, 20

Distribution planning, and inventorymanagement, 20–21

Disutility, measure for, 177Dobson, G., 103, 161–162, 165, 178, 185Dogru, M. K., 218Dong, L., 225Downs, B., 103Doyle P., 105, 143, 158Dreze, X., 102, 242Drop-shipping channel, 125Dudey, M., 293, 294Duopoly competition, 130DVD player data base, illustrative

application for, 198comparing model’s predictions to

retailer’s sales data, 199impact of customer preference structure,

200–203optimal assortment vs. expected revenue

of retailer’s assortment, 200Dynamic assortment planning, 126–127Dynamic markdown competition, 294

See also Markdown competitionDynamic pricing, 273, 274Dynamic pricing models, variety of, 293Dynamic substitution, 115, 116, 123

variety under, 124

E

Echelon stock, 213of warehouse, allocation of, 214

Economic order quantity (EOQ) model,103, 142

cost function in, 115, 128for incorporation of economies of

scale, 103for inventory levels decisions at Tanishq,

141, 142on inventory turnover, 26replenishment costs as explained by, 39

Economies of scale and scopearguments of, 44, 48diminishing, 44drivers of, 39effects of, 43factors contributing to, 26hindrances to, 38realization in transportation costs, 39in retail setting, 27, 28

Efficient consumer response (ECR),category management ascomponent of, 156

El-Ansary, A. I., 156Electronic data interchange (EDI), 21, 211Eliashberg J., 103, 275Elmaghraby, W., 96, 273–274Emma, C. K., 65Emmelhainz, L. W., 53, 108, 157, 164Emmelhainz, M. A., 53, 108, 157, 164Employee error, 56, 59

examples of, 57, 58Employee incentives, 68Employees

demoralization of, 61nonconformance among, misplacement

of products due to, 57, 60role in product availability management

efforts, 62–63employee turnover and training,

60–61employee workload, 61–62

Employee turnover, 68and phantom products, relationship

between, 73problems of, 61and training, influence on product

availability management, 60–61EOQ, see Economic order quantity (EOQ)

modelEppen, G. D., 26, 38, 215, 220Epple, D., 61

314 Index

Erkip, N., 215, 227Erlenkotter, D., 170Estimation and empirical testing, 190Estimators, ordinary least squares (OLS)

estimators, 73Evers, J., 294Execution

poordrivers of, 58sources of, 56

problems, strategies for reduction ofoccurrence, 68

and product availability management inretail, 53

factors exacerbating problems, 58–63influence on inventory planning,

63–66problems in, 54–58research opportunities in, 66–69

Exogenous demand models, 110–113assortment planning under, 117

Kok and Fisher model, 119–123Smith and Agrawal model, 118–119

substitution rates in, demand estimationof, 135–137

Expectation-Maximization(EM) algorithm,for correction of missing data,132, 136

F

Fader, P. S., 127, 131Fall season, 14Fama, E. F., 29Farris, P., 105Fashion products, 225–226

model with ‘demand trajectory’ for, 295Federgruen, A., 6, 207–208, 215, 221, 227Feng, Y., 274, 293FIFO (first in first out), inventory valuation

method, 32Firms

de-listing of, 35lifecycle, effects on model estimation, 50size

correlation with inventory turnover,26, 27, 37, 39

effect of inventory turnover on, 38–40See also Inventory turnover,

performance, effects of firm sizeand sales growth rate on

Fisher, M. L., 5, 26, 42, 60, 99, 118, 119, 121,126, 132, 136, 161, 169–170, 184,220, 226

‘‘Fixture fill,’’ minimum on-handinventory, 276

Flagship brand, 17Fleisch, E., 66Flores, B. E., 54Forecast accuracy, 67

sensitivity of shelf space allocationmodels to, 105

Freeland, K., 137French, K. R., 29French, S., 167Furniture retail, importance of DC in, 14

G

Gallego, G., 274, 279, 293, 296Galway, L. A., 54Gamma Corporation

drivers of inventory record inaccuracy in,research analysis on, 69–71

inventory record inaccuracy in, 63The Gap stores, 13Gaukler, G. M., 66Gaur, V., 3, 25, 26, 29, 38, 42, 123, 160Gavirneni, S., 221General Mills, as category captain in baking

ingredients and mixes category, 81Gerchak, Y., 228Gershwin, S. B., 66Gerstner, E., 241GFR [Gaur, Fisher and Raman (2005)], 36,

37, 48on inventory turnover performance of

U.S. retailers, 26use of, 27

re-test of hypotheses in, 42Gilbert, S. M., 96Gilligian, T., 86Glasserman, P., 228Goyal, S. K., 104Grabner, J., 108Graves, S. C., 216, 217, 231Graves, S. G., 63Greenberger, R. S., 82Greene, W. H., 132Green, P. E., 185, 198Grocery industry, Albert Heijn,

replenishment system at, 119Gross margin (GM), 26, 27, 36, 37

correlation with inventory turnover, 29Gruca, T. S., 131Gruen, T. W., 53, 107, 108, 157Guadagni, P. M., 109, 130–131Guar, V., 184, 193

Index 315

Gul, F., 304Gupta S., 5, 131, 133, 155, 168

H

Ha, A., 228Hadley, G., 103Hall, R. V., 59Hanks, C. H., 54Hanson, 186Hardie, B. G. S., 127, 131Harris, B., 167Hart, M. K., 54Hauser, J. R., 157, 164Hausman. W. H., 125, 148, 185Hayek, F., 305Hayen, R., 29Hedonic products, 106Hendricks, K. B., 28Henig, M., 228Herer, Y. T., 223Hess, J. D., 241Hoch, S. J., 106Holding costs, 213, 217, 220Hollinger, R. C., 58Home furnishings

concept of brand retail marketing, 11supply chains, characteristics of, 13

Honhon, D., 123, 160, 184, 193Hotelling, H., 102, 113Hotelling model, 102

See also Locational choice modelHo, T.-H, 128Huchzermeier, A., 96Huffman, C., 106Human discomfort index (HDI), 134Huson, M., 29

I

Ide, E. A., 128Iglehart, D. L., 65Imai, K., 163, 172Infinitesimal perturbation analysis, 223Information sharing alliance, 84, 85, 86Information systems for retailers, Circa

1990, 1Inspection policy, 65Internet channel, 13, 14

customers from, shipment ofmerchandise to, 20

deeper price markdowns in, 21Internet retailing, drop-shipping

channel in, 125Internet sessions, 183

Intertemporal pricing, 274Inventory

agreements, vendor-managed, 149allocation

policies used at warehouse, 212solution methodologies for issue of,

214–218as asset of retail firm, 25costs, 242counts, optimal frequency of, 65levels

and product availability, relationshipbetween, 67

role in product availabilitymanagement, 59–60

ordering decision, 217See also Inventory decisions

pooling, 305productivity

drivers of, 50of retail firms, benchmarking of, 26

productivity performance, 25record accuracy, high, in Arrow

Electronics, mechanism for, 59See also Inventory record inaccuracy

(IRI)replenishment, 60shrinkage, 62system and actual, absolute value

difference between, 55team, responsibility of, 17theory, newsboy model in, 49valuation methods, 32vendor managed, 1

Inventory decisions, 156aggregate-level, modeling of, 50factors involved in, 101

Inventory imbalance, 209Inventory management, 20–21

of multiple products, 103in retail trade, importance of

improvement in, 25Inventory management decisions, 21

See also Retail supply chainmanagement, multi–locationinventory models for

Inventory modelsgeneral periodic review, 212

additional issues, 227–228batch ordering, 218decentralized environments, 220–221fashion products, 225–226lateral pooling, 221–225

316 Index

lost sales, 219–220solution methodologies, 214–218transportation issues, 226–227

multi-item, 103–104multi-location, for retail supply chain

management, 207modeling issues, 208–212

Inventory planningand assortment, 4–6and assortment selection

in decentralized supply chains, 125dynamic, 126–127under exogenous demand models,

117–123under locational choice, 123–124with MNL: van Ryzin and Mahajan

model, 114–117models with multiple categories,

127–130effect of inventory record inaccuracy on,

63–64effect of misplaced products on, 64research focus on, 4

Inventory record inaccuracy (IRI), 4, 53, 55effect on inventory planning, 63–64impact of, mitigation of, 66of SKU, 69theft as source of, 66

Inventory turnover (IT)adjusted (AIT), 29correlation with firm size, 26, 27correlation with firm size and sales

ratio, 37effect of

firm size on, 38–40sales ratio on, 40–42

performance, across firms,differences in, 49

in performance analysis, 3performance, effects of firm size and sales

growth rate on, 25adjusted inventory turnover, 36–37data description, 30–36directions for future research, 50firm size effect on, 38–40hypotheses, 37–42literature on, 28–29model, 42–43results, 43–48sales ratio effect on, 40–42

variation in, 26variation of, 35

Invoice accuracy, 63

IPACE (information, price, assortment,convenience and entertainment)model, for retail shoppingdecisions, 186

Irion, J., 105Ittner, C. D., 60Iyer, A. V., 96Iyogun, 227

J

Jackson, P. L., 215, 227Jain, D. C., 165Jennifer Convertibles, Inc, 50Jewelry

Indian, 101See also Tanishq

market, India’s, 140products, 44

Johnson, P. L., 157, 164Joint fixed costs, 178Joint inventory and pricing decisions,

literature on, 96Joint pricing and assortment planning, 147Joint replenishment effect, 209Joint replenishment problem, 226, 227Jonsson, H., 215, 224Just-in-time (JIT)

manufacturing, 59principles, adoption of, 28, 29

K

Kadane, J. B., 164Kahn, B. E., 106, 190Kalish S., 103, 161, 163–164, 165, 178, 185,

274, 275, 279Kalyanam, K., 23, 186, 243, 275, 279Kamakura, W. A., 165Kamien, 278Kang, Y., 66Kapuscinski, R., 242Karlin, S., 162Karmarkar, U. S., 222Kenny, D., 88, 102Kesavan, S., 3, 25, 50Keskinocak. P., 96, 273–274Kim, S. Y., 96Kleywegt, A., 227Koenig, S., 239Kohli, R., 124, 185Kok, A., 5, 63, 161, 184, 192, 216, 227,Kok and Fisher model, for inventory

planning and assortment selection,119–123

Index 317

Kouvelis, P., 178Krafcik, J. F., 59Krajewski, L. J., 54Kreps, D., 298Krieger, A. M., 185Krishna, A., 164Krishnan, H., 242Krishnan, K. S., 222Kuhn-Tucker theorem, 297Kurtulus, M., 4, 79, 83, 86, 88, 89,

90–91, 93, 125

L

Lagrangian relaxation approach, 119Lal, R., 96, 242, 294Lancaster, K., 102, 113, 184Langton, L., 58Lariviere, M. A., 210, 274Lateral pooling, 221–225Lattin. J. M., 128, 188, 190Laudon, K. C., 54Lazear, E. P, 274, 293Lead time(s), 17

design-to-shelf, at Mango (Spain), WorldCo. (Japan), and Zara (Spain), 126

types of, 211for upholstery or fabrics, 18

Lean production system, 68Lee, H. L., 81, 162, 220, 221, 222Lee, S. M., 29Lee and Wrangler, as category captain jeans

category, 81Lehmann, D. R., 157, 190Lerman S. R., 109, 110, 129, 187, 189, 190Levitan, R. E., 83–84Levy, M., 137, 146Li, C.–L., 243Lieberman, M. B., 29LIFO (last in first out), inventory valuation

method, 32Lin, C. T., 220Lippman, S. A., 104, 224Liquidation options, 21Little, J. D. C., 109, 130–131, 167, 242Little’s law, 216Liu, P., 108Locational choice demand model,

assortment planning under,123–124

Locational choice model, 113Lockers, off-site, 21Logistics planning, of supply chain, 19–20Loss, due to misplaced products, 53, 64

Lost sales, 59, 219–220causes of, 66estimate of, 67modeling, complexity of, 211for unmet store demands, 211

Louviere, J., 164Luenberger, D. G., 301, 306Lundholm, R., 304

M

McBride, R. D., 124, 161, 165, 185McCardle, K. F., 104, 224McClain, J. O., 54McCullogh, R., 163, 172McCutcheon, C., 54McFadden, D., 131McGavin, E. J., 212, 215, 219, 227McGillivray, A. R., 104McGuire, T. W., 83–84McPartland, M., 167Maddah, B., 117Mahajan S., 91, 101, 104, 109, 114, 115,

116–117, 119, 123, 125, 132, 160,162, 184, 185, 193, 195, 279

Makridakis, S., 40Malhotra, A., 28Manchanda, P., 128Mango (Spain), design-to-shelf lead

time at, 126Mantrala, M. K., 275Manufacturer-retailer partnerships, 95Manufacturers

non-captain, 90non-captain manufacturers, 90non-partnering, 95as Stackelberg leader, 85, 87

Manufacturing process, time taken for, 18MARK, decision support system, 275Markdown competition, 293, 298–303

managerial implications, 303–305model, 295–296monopolistic retailer, 296–298

Markdown optimization, in supply chainplanning, 21

Markdown planning, 21importance in pricing, see Clearance

pricing in retail chainsMarketing

channels, direct-to-consumer, 12cross-channel, 22trade promotions in, literature on, 96

Martello, S., 22Mathur, K., 227

318 Index

Maximum likelihood estimates (MLE),131, 136

Mean absolute deviation (MAD), 135Menzies, D., 256Merchandise

categories of, 17optimal pricing policy for, properties

of, 289paths for, 15strategic trends in retail, 272

Merchandising constraints, 183Merrick, A., 271Mervyns, 13METRIC approximation, 229, 230Micro-merchandising, 228Mierswinski, E., 54Miller, C. M., 117, 161, 162Millet, I., 54Millman, H., 238Misplaced products, 4, 53, 54

effect on inventory planning, 64influence on product availability

management, 56, 64Mixed integer program (MIP), 105MNL model, see Multinomial logit (MNL)

modelMobley, W., 61Model description, for optimization of

assortments suiting diversecustomer preferences, 185

modeling consumer’s purchase decision,186–191

optimal assortment, properties of,195–197

optimization problem, solving of,197–198

retailer’s assortment optimization,191–195

Model estimation, effects of firm lifecycleand bankruptcies on, 50

Modelingof aggregate-level inventory decisions, 50of demand, 209–211issues, in retail supply chain management

allocation policies used atwarehouse, 212

key decision, 208–209lead times, 211modeling demand, 209–211

Modelsfor clearance pricing in retail chains

formulation, 277–281mathematical models for, 273

specifications and optimalityconditions, 276–281

of markdown competition, 295–296See also Specific models

Mondschein, S. V., 274Monopolist retailer, 185, 296–298

with high inventory, 298optimal strategy for, 196shaping of demand by, 305

Monopolization, 82Moorthy, S., 102–103Morey, R. C., 65Morgan, L. O., 178Mowday, R., 61Muckstadt, J., 215, 222, 228Muller, E., 279Multi-item inventory models, 103–104Multilocation inventory models,

complexity of, 212Multinomial logit (MNL) model, 91,

108–110, 161assortment planning with, 114–117as choice model, 106, 160, 187classical, 162for customer’s selection of product and

retailer, 183demand estimation of, 130–135with homogeneous expected utilities, 184MCI model, as alternative to, 131optimal assortments under, 124predictive accuracy of utilities in, 199substitution with, 104

Multiplicative competitive interactions(MCI) model, 131

Multi-store, assortment planning in, 148Mussa, M., 102–103Myopic allocation

method, 214policy, 217

N

Naert, P., 105, 158–159Nahmias. S., 6, 67, 162, 208, 219Nakanishi, M., 131, 134Nanda, D., 29Narasimhan, C., 83, 84, 241, 275National Retail Federation, 61Negative binomial distribution (NBD), 118,

119, 216, 231Nelson, P., 163–164Nelson, R., 61Nemhauser, G., 169Nested logit model, 110

Index 319

Netessine, S., 29, 103, 104, 275, 295New products, assessment of market

potential of, 22Newsboy model, in inventory theory, 49Newsvendor model, 26

cost function from, 116for rebates in retail, seeRebates, in supply

chain, manufacturer-to-retailer vs.manufacturer-to-consumer

Nguyen, D., 109Nie, W. D., 54Niraj, R., 83, 84Non-basket shopper, 129Non-captain manufacturers, 90Noonan, P. S., 104No-purchase decision, 170

modeling, 168–169operationalization of, 165, 166utility of, 164

Nunes, J. C., 102, 156, 159–160, 164

O

Old Navy, 13Olenick, D., 239Online shopping, 15Operations management, 6

focus of research in, 53Optimal assortment, 174–177

properties of, 195–197See also Assortments optimization,

for diverse customer preferencesselection, 90sensitivity to input assumptions, 204

Optimal container packing, 22Optimal discrete pricing, 281, 284Optimal initial price, 287Optimal inventory, and maximum profit,

determination of, 281Optimal inventory policy, 66Optimal joint inspection, and replenishment

policy, 65Optimal pricing policy for merchandise,

properties of, 289Optimal stocking, 116Optimal stocking policy, 218Optimization model

additional retailer constraints for, 203discussion of, 170–171

Ordering decision, 213Order-up- to policy, 219Ordinary least squares (OLS) estimators, 73Oren, S., 274, 279Ornamentation assortment, 12

Outlet stores, 14deeper price markdowns in, 21

Out-of-stocks (OOS), consumerresponse to, 108

Overstreet, T., 86Overtime, 3, 26

retailer, 48

P

Packaging, redesigning for costsaving, 20

Pantumsinchai, P, 227Parlar, M., 104, 224Partnerships, manufacturer-retailer, 95Pashigian, B. P., 274Past priority allocation (PPA), 217Patel, N. R., 222Pazgal, A., 293Penalty cost, 213, 220Penalty for disutility, 5Pentico, D., 160Perfect competition, 203Performance

across retail stores, 58differences among firms and overtime,

causes of, 3of retailers, importance of

inventory in, 25Permanent assortment reductions (PAR),

consumer response to, 108Petruzzi, N. C., 96, 243Phantom products

and employee turnover, relationshipbetween, 61, 73

and employee workload, relationshipbetween, 72

high percentage of, 60and store manager turnover, relationship

between, 72and training, relationship between, 73

Phantom stockout, 56Physical inventory, 59‘‘Pick and pack’’ warehouse, 14, 20Pieters, R., 106Plambeck, E., 20Planning processes, in supply

chain, 15clearance and markdown

optimization, 21cross-channel optimization, 22distribution planning and inventory

management, 20–21logistics planning, 19–20

320 Index

product design and assortment planning,17–18

sourcing and vendor selection, 18Point-of-sale (POS) scanner systems,

6, 208Porteus, E. L., 7, 210, 237, 243, 245, 274Predictions, top-down and bottom-up, 16Price changes, discrete, 281

optimal discrete pricing, 284solution for power function form,

282–285Price lookup (PLU) codes, 57Price optimization, integration into retail

supply chain decisions, 6–8Pricing, 229

clearance pricing in retail chainsdifference from other types of retail

pricing decisions, 276discrete price changes, 281–285mathematical models for, 273model specifications and optimality

conditions, 276–281numerical examples, 285–289related research, 273–276trends in, 271–273

dynamic pricing models, variety of, 293joint inventory and pricing decisions,

literature on, 96optimal pricing policy for merchandise,

properties of, 289problem, 178solutions, continuous and discrete,

comparison of, 285Pricing decisions, 21

delegation of, 86–90strategic, 187

Probabilistic inventory record, 65Probit demand model, household parameter

estimates of, 172Process design, impact of, 68Product availability

impact of store execution on, 66and inventory levels, relationship

between, 67Product availability management, 53

influence of product variety on, 60research opportunities, 66–69retail execution problems, 54

factors exacerbating, 58–63incorporation into existing research

streams, 63–64influence on inventory planning,

63–66

inventory record inaccuracy, 55,63–64

misplaced products, 56, 64root causes of, 56–58

role of execution in, see Retail executionproblems, in product availabilitymanagement

Product choices, narrowing of, 188Product design, and assortment planning,

17–18Product design process architecture, 18Product line design, 102–103Product(s)

misplacement, see Misplaced productspositioning, 103seasonal, 192

Product varietyeffect on supply chain structures, 125influence on product availability

management, 60and inventory levels, effect on store sales

in Borders Group, 73–75perception of, 106and product line design, 102–103on retail shelf, management of, 155

assortment problem, solutiontechnique for, 173–174

assortment and stocking problem,165–171

consumer model, 162–165future work, 177–178household scanner panel data,

description of, 171–173literature on, 158–162optimal assortment, 174–177

Profitability, influence of shrinkage ofproducts on, 62

Profit loss, for incorrect consideration setassumption, 203

Profitsunder adjacent/random substitution

structure, 128basket profits, 130expected, effect of consideration sets on,

202maximum with optimal inventory,

determination of, 281reduced, due to misplaced

products, 53Promotion planning, 92Promotions, cross channel impacts of, 23Proschan, F., 244Pryor, K., 227

Index 321

Pull price promotions, see Consumerrebates

Purchase decision(s), 148about grocery and health-and-beauty

products, 157as involving substitution, 100modeling of, 186stages of, 189See also No-purchase decision

Purchase-incidence, 133, 134Push price promotions, see Retailer

rebates

Q

Quelch, J. A., 88, 102

R

Rajagopalan, S., 28Rajan, 274, 275, 279Rajaram, K., 104, 122–123, 226Raju, J. S., 242Rakesh, 274, 279Ralph Lauren, 13Raman, A., 26, 42, 53, 55, 56, 58, 59, 60–61,

62, 64, 65, 66, 67, 68, 69, 71, 73,126, 136

Rao, R. C., 242, 294Rao S., 275Rao, V. R. K., 222Rappold, J. A., 228Ray, S., 275Reason, J., 58Rebates

in supply chain, manufacturer-to-retailervs. manufacturer-to-consumer,237

consumer rebate only, 249–252consumer and retailer rebates

together, 243–247literature on, 241–243numerical examples, 252–255retailer rebate only, 247–249

types of, 239, 240Record inaccuracy

alternative solution to, 65see Inventory record inaccuracyshortages caused by, protection

against, 65Redistribution decision, 224Redman, T., 54Reinman, M., 227Rekik, Y, 66Relative sales per title (RST), 140, 143, 144

Replenishment, 60, 211attribute-focused rather than product-

focused, 146costs, 39joint replenishment problem, 226, 227lead time, 209policy, 65

Resale price maintenance (RPM), 86Re-stocking, 63Retail assortments, optimization for diverse

customer preferences, 183DVD player data base, illustrative

application for, 198comparing model’s predictions to

retailer’s sales data, 199impact of customer preference

structure, 200–203optimal assortment vs. expected

revenue of retailer’sassortment, 200

model description, 185modeling consumer’s purchase

decision, 186–191optimal assortment, properties of,

195–197optimization problem, solving of,

197–198retailer’s assortment optimization,

191–195Retail category management, see Category

captainship; Product variety onretail shelf, management of

Retail chains, clearance pricing indiscrete price changes, 281

power function form, solution for,282–285

mathematical models for, 273model specifications and optimality

conditions, 276model formulation, 277–281

numerical examples, 285–289related research, 273–276trends in, 271–273

Retailer assortment and stocking problembasic formulation, 165–168modeling no purchase, 168–169optimization model and some special

cases, 170–171reformulation, 169–170

Retailer decisionsassortment decision, 156inventory decision, 156strategic, basis for, 184

322 Index

Retailer rebates, 240push price promotions, 241in supply chain, 247–249use of, 238

Retailer(s)customer’s choice of, 185decision making process, 23growth rate of, factors restricting, 41market potentials of, 41market strength, sensitivity to, 196–197monopolistic, 203, 296–298non-identical, 215, 216, 218overall objective function for, 167perfect competition among, 185

Retail execution problems, in productavailability management

factors exacerbating, 4employee effort, 62–63employee turnover and training,

60–61employee workload, 61–62inventory levels, 59–60product variety, 60

influence on inventory planning, 4incorporation of execution problems

into existing research streams,65–66

inventory record inaccuracy, 55,63–64

misplaced products, effect of, 56, 64root causes of, 4, 56–58

Retail firm, assets of, 25Retail industry, average employee turnover

in, 61Retail inventory management, see Retail

supply chain management, multi-location inventory models for

Retail management, academic research focusin, 2–8

Retail master calendar, 16Retail performance, suboptimal,

causes of, 66Retail shopping decisions, iPACE

model for, 186Retail store demand, modeling of, 209–211Retail stores, aggregate echelon inventory

positions of, 214Retail supply chain, 14

decisions, integration of priceoptimization into, 6–8

practices, empirical studies of, 3–4Retail supply chain management, crucial

areas of, 2–3

Retail supply chain management, multi-location inventory models for, 207

modeling issuesallocation policies used at

warehouse, 212key decision, 208–209lead times, 211modeling demand, 209–211

periodic review inventory model,general, 212

additional issues, 227–228batch ordering, 218decentralized environments, 220–221fashion products, 225–226lateral pooling, 221–225lost sales, 219–220solution methodologies, 214–218transportation issues, 226–227

Retail Workbench, at Santa Clarauniversity, 2

Return on assets (ROA), effect of JITadoption on, 29

RFID technology, in reducing executionerrors, 66

Rhee, B.-D., 243Ricadela, A., 239Rinehart, R. F., 54Risk pooling, 38

advantage of, 209advantages of, 215benefits of, 125

Roberts, J. H., 188, 190Robinson, L. W., 222, 223Rosen, S., 102–103Rossi, P. E., 163, 172Ross Products, as category captain for

Safeway in infant formulacategory, 81

Rout, W., 54Rudi, N., 104, 224, 225Ruiz-Diaz, F., 167Rumyantsev, S., 29Russell, G. J., 128, 165, 275Ryan, J. K., 147

S

Sack, K., 25, 26Safety stock, 38, 218Sales contraction region, 41, 46Sales expansion region, 41, 46Sales growth

on inventory turnover, effect ofvolatility in, 47

Index 323

Sales growth rate, 40and inventory turnover, relationship

between, 26Sales ratio

correlation with inventory turnover,27, 37

on inventory turnover, effect of volatilityin, 47, 48

negative effect on inventory turnover,40, 41

positive effect on inventory turnover,40, 41

regions of, 41in sales contraction/expansion region, 48

Sales surprise (SS), 26, 27, 36, 37correlation with inventory turnover, 29

Samroengraja, R., 216Scarf, H., 162, 215Schary, P., 108Schmidt, C. P., 216Schonberger, R. J., 59Schrady, D. A., 54Schrage, L., 26, 38, 215Schroeder, L., 43Schwartz, N., 278Sethi, S. P., 275Shang, K. H., 65Shapiro, J., 167Shelf space

allocation models, 104–106sensitivity to forecast accuracy, 105

availability, 5constraints, 178Corstjens and Doyle model for allocation

of, 105and mindspace, battle for, 95

Sheppard, G. M., 54Sherali, H. D., 178Sherbrooke, S. C., 229Shipments, 216

direct-to-consumer, 14Shipping

needs, for retailers, 19to stores, frequency of, 21

Shmueli, G., 164Shoemaker, R. W., 164Shopping

basket shopping consumers, 128, 129decisions, iPACE model for, 186fixed and variable costs of, 128non-basket shopper, 129

Shrinkage of products, influence on storeprofitability, 62

Shubik, M. J., 83–84Shumsky, R., 295SIC code, see Standard industry

classification (SIC) codeSiddarth, S., 188Silver E. A., 38, 104, 215, 224, 227Simchi-Levi, D., 227, 243Simonson, I., 106Singhal, V. R., 28Singh, P., 125, 148–149Skinner, W., 60SKU-day-store, 133SKU, see Stock keeping units (SKU)Sloot, L., 160Slow sellers, 15Smith and Agrawal model, for inventory

planning and assortment selection,118–119

Smith S. A., 1, 3, 5, 6, 7, 11, 67, 112, 117,118–119, 128, 148, 160–161, 169,183, 184, 193, 207, 208, 209–210,219, 226, 228, 271, 275, 276, 278,279, 281, 289

Song, J.-S., 104Sourcing

agents, 17and vendor selection, 18

Space allocation, inter/intra-category, 156Speranza, M. G., 227Srinivasan T. C., 188Srinivasan, V., 198, 242Stackelberg leader, 85, 87, 225Staelin, R., 83–84, 96Standard & poor’s compustat database,

28, 30Standard industry classification (SIC) code,

30, 31Staw, B., 61Steers, R., 61Steinberg, R., 103, 274, 275, 279Steiner, R. L., 80, 93Stern, L. W., 156Stock

allocation, 215cycle, 38returns, inventory turnover performance

with, 29safety, 38

Stocking, 53Stocking decisions, 103

for retail category management, 177Stocking inventory, back-rooms for, 21Stocking quantity, 63

324 Index

Stock, J. R., 157, 164Stock keeping units (SKUs), 1, 14, 69

at Albert Heijn, groups of, 143at Best Buy, 139at Borders Group Ink, 139categories of, 12categorization in retail, 99inter-relationships among, 3, 11, 12on move, and in stock with Transworld

Entertainment, 105ornamentation, 12rationalization efforts in General

Mills, 81retailer’s breadth/depth, 100at Tanishq, 140

on replenishment, 141, 142Stockout-based substitution, 103, 107, 117,

160, 193estimation of, 135–136in exogenous demand model, 113

Stockoutsconsumer response to, 107, 108forecast accuracy in the presence of, 67lost revenue due to, 64phantom stockout, 56resulting from poor inventory planning/

execution, 59Stokey, N., 274Storage capacity, 68Storage needs, additional, 21Store-bound merchandise, 14Store execution

impact on product availability, 4, 66poor, consequences of, 69poor, examples of, 53and product variety, relationship

between, 60and storage area size, relationship

between, 68Store inventory, 54Store manager turnover, and phantom

products, relationship between, 72Store sales

effect of product variety and inventorylevels on, study in Borders Group,73–75

vs. customer entrances, 64Stulman, A., 220Substitution

assortment based, 100assortment-based, 107, 117, 123

estimation of, 136–137assortment-based, estimation of, 136–137

behavior, of consumers, 157, 160between brands, asymmetries in patterns

of, 173consumer driven, 107–108between consumers, heterogeneity in

patterns of, 173dynamic, 115, 116, 123

variety under, 124incremental demand arising from,

192–193involving, 100rates in exogenous demand models,

demand estimation of, 135–137in retail, classification of, 107stocking, adjacent, 128stockout-based, 100, 103, 107, 117, 118,

123, 160estimation of, 135–136

Sudharshan, D., 131Sugrue, P. K., 210Sukumar, R., 124, 185Supply chain, 11

description, 13–15performance, opportunities for

improvement of, 23planning processes, 15

clearance and markdownoptimization, 21

cross-channel optimization, 22distribution planning and inventory

management, 20–21logistics planning, 19–20product design and assortment

planning, 17–18sourcing and vendor selection, 18

planning processes, details of, 3rebates in, 237

consumer rebate only, 249–252consumer and retailer rebates

together, 243–247literature on, 241–243numerical examples, 252–255retailer rebate only, 247–249

structures, effect of productvariety on, 125

Supply chain management,constraints on, 13

Supply chain management, multi-locationinventory models for, 207

modeling issuesallocation policies used at the

warehouse, 212key decision, 208–209

Index 325

Supply chain management (cont.)lead times, 211modeling demand, 209–211

periodic review inventory model,general, 212

additional issues, 227–228batch ordering, 218decentralized environments,

220–221fashion products, 225–226lateral pooling, 221–225lost sales, 219–220solution methodologies, 214–218transportation issues, 226–227

Supply chain profitsconstant-split property of

consequence of, 250rationale behind, 251

split under consumer rebates, 240–241Supply chains

decentralized, assortmentplanning in, 125

Svoronos, A., 218Swait, J., 164Swaminathan, J. M., 66System inventory, 54, 59

and actual inventory, absolute valuedifference between, 55

T

Tagaras, G., 222–223Tallman, J., 54Talluri, K., 132, 135–136, 293Tang, C. S., 104, 128Tanishq (Indian jewelry retailer), assortment

planning in practice, 140–142Target stores, 13Taylor, T. A., 30, 103, 238, 242Tayur, S., 63, 228Tellkamp, C., 66Theft, as source of inventory record

inaccuracy, 66Third party logistics (TPLs), 15Todor, W., 61Toktay, L. B., 4, 79, 83, 86, 88, 89,

90–91, 93, 125Ton, Z, 3–4, 53, 56, 58, 59, 60, 61, 62, 63, 64,

67, 68, 69, 71, 73Top-down analysis, 16TQM principles, implementation of, 28Tractability, assortment planning models

for, assumption made in, 112‘‘Traffic generators,’’ 167

Training, 68and phantom products, relationship

between, 73Transportation costs, 39Transportation issues, in retail supply chain

management, 226–227Transshipments, 221–225Trucks, use in delivery of shipments,

20, 21, 23Tsay, A. A., 6–7

U

Ukovich, W., 227United States Tobacco Co. (UST), antitrust

row with Conwood, 82Unmet demand

backlogged, 212backordering, 211, 216, 218, 221, 231lost sales, 120, 219, 240meeting of, 222, 223

Urban, G. L., 157, 164Urban, T. L., 105U.S. retail sector, effects of firm size and sales

growth rate on inventory turnoverperformance in, 25

adjusted inventory turnover, 36–37data description, 30–36directions for future research, 50hypotheses, 37

effect of firm size on inventoryturnover, 38–40

effect of sales ratio on inventoryturnover, 40–42

literature on, 28–29model, 42–43results, 43–48

Utility, of purchasing from retailer, 189

V

Vaidyanathan, R., 99Van Dijk, A., 159Van Donselaar, K., 215Van Dyck, D. A., 163, 172Van Herpen, E., 106Van Ryzin, G., 91, 101, 104, 109, 114, 115,

116–117, 119, 123, 125, 132,135–136, 160, 162, 184, 185, 193,195, 274, 279, 293, 296

Van Ryzin and Mahajan model,114–117

demand process in, 132Variables, for each retailing segment,

summary statistics of, 33–34

326 Index

Varietyconsumers’ perception of, 147perception of, 106See also Product variety

Vendor capacity planning, 18Vendor managed inventory, 1Vendors, 17

brand management by, 5social compliance by, 18

Vendor selection, 18Venkataramanan, M. A., 155Verrijdt, J. H. C. M., 227Vilcassim, N. J., 165Villas-Boas, J. M., 96, 242Virtual allocation policy, 212, 216Visual and marketing group, 17Vives, X., 84Vlachos, D., 223Volatility, effect in sales ratio on inventory

turnover, 47, 48

W

Walmart, 20Walter, C., 108Wang, Y, 83–84, 86, 87, 88, 90Warehouse

allocation policies used at, 212clubs, growth of, 156demand

approximation of, 216variability, 218

echelon stock of, allocation of, 214holding costs, 217periodic replenishment at, 215‘‘pick and pack’’, 14, 20space, sharing of, 14stock-less, 215

Wecker, W. E., 67Weeks-of-supply (WOS), 21Weitz, B. A., 137, 146Whang, S., 8, 293

Wharton research data services (WRDS), 30Wheelwright, S. C., 40White, E., 60–61Whitin, T. M., 103Whybark, D. C., 54Wijngaard, J., 215Wilson, L. W., 54Winston, W. L., 274Winter, S., 61Wittink D. R., 198Woellert, L., 54Wolfe, H. B., 274Woolsey, G., 54Working capital management, 3Workload, 68World Co. (Japan), design-to-shelf lead

time at, 126Wrangler and Lee, as category captain jeans

category, 81Wu, C. F. J., 132

X

Xiao, B., 293

Y

Yano, C., 96Ye, J., 96Young, S. T., 54

Z

Zara (Spain), design-to-shelf leadtime at, 126

Zhao, H., 225Zhao, W., 274Zheng, Y. S., 215, 227, 231Zheng, Y-S, 274Zinn, W., 108Zipkin, P. H., 104, 215, 218, 227Zotteri, G., 67Zufryden, F. S., 124, 161–162, 165, 185

Index 327

Early Titles in theINTERNATIONAL SERIES INOPERATIONSRESEARCH&

MANAGEMENT SCIENCE(page ii)

Axsater, S. / INVENTORY CONTROLWolkowicz, H., Saigal, R., & Vandenberghe, L. / HANDBOOK OF SEMI-D-

EFINITE PROGRAMMING: Theory, Algorithms, and ApplicationsHobbs, B.F. & Meier, P. / ENERGY DECISIONS AND THE ENVIRON-

MENT: A Guideto the Use of Multicriteria MethodsDar-El, E. / HUMAN LEARNING: From Learning Curves to Learning

OrganizationsArmstrong, J.S. / PRINCIPLES OF FORECASTING: A Handbook for Rese-

archers andPractitionersBalsamo, S., Persone, V., & Onvural, R./ ANALYSIS OF QUEUEING NET-

WORKS WITHBLOCKINGBouyssou, D. et al. / EVALUATION AND DECISION MODELS: A Critical

PerspectiveHanne, T. / INTELLIGENT STRATEGIES FOR META MULTIPLE CRI-

TERIA DECISION MAKINGSaaty, T. & Vargas, L. /MODELS,METHODS, CONCEPTS and APPLICA-

TIONS OF THE ANALYTIC HIERARCHY PROCESSChatterjee, K. & Samuelson, W. / GAME THEORY AND BUSINESS

APPLICATIONSHobbs, B. et al. / THENEXTGENERATIONOF ELECTRIC POWERUNIT

COMMITMENT MODELSVanderbei, R.J. / LINEAR PROGRAMMING: Foundations and Extensions,

2nd Ed.Kimms, A. / MATHEMATICAL PROGRAMMING AND FINANCIAL O-

BJECTIVES FOR SCHEDULING PROJECTSBaptiste, P., Le Pape, C. & Nuijten, W. / CONSTRAINT-BASED

SCHEDULINGFeinberg, E. & Shwartz, A. / HANDBOOK OF MARKOV DECISION PRO-

CESSES: Methodsand ApplicationsRamık, J. & Vlach, M. / GENERALIZED CONCAVITY IN FUZZY OPTI-

MIZATION AND DECISION ANALYSISSong, J. & Yao, D. / SUPPLY CHAIN STRUCTURES: Coordination, Infor-

mation and OptimizationKozan, E. & Ohuchi, A. / OPERATIONS RESEARCH/ MANAGEMENT

SCIENCE AT WORKBouyssou et al. / AIDING DECISIONS WITH MULTIPLE CRITERIA: Es-

says in Honor of Bernard Roy

Cox, Louis Anthony, Jr. / RISK ANALYSIS: Foundations, Models andMethods

Dror, M., L’Ecuyer, P. & Szidarovszky, F. / MODELING UNCERTAINTY:An Examination of Stochastic Theory, Methods, and Applications

Dokuchaev, N. / DYNAMIC PORTFOLIO STRATEGIES: Quantitative Me-thods and Empirical Rules for Incomplete Information

Sarker, R., Mohammadian, M. & Yao, X. / EVOLUTIONARYOPTIMIZATION

Demeulemeester, R. & Herroelen, W. / PROJECT SCHEDULING: A Resea-rch Handbook

Gazis, D.C. / TRAFFIC THEORYZhu/ QUANTITATIVE MODELS FOR PERFORMANCE EVALUATION

AND BENCHMARKING Ehrgott & Gandibleux/ MULTIPLE CRITERIAOPTIMIZATION: State of the Art Annotated Bibliographical Surveys

Bienstock/ Potential Function Methods for Approx. Solving Linear Program-ming Problems

Matsatsinis & Siskos/ INTELLIGENT SUPPORT SYSTEMS FOR MAR-KETING DECISIONS

Alpern & Gal/ THE THEORY OF SEARCH GAMES AND RENDEZVOUSHall/HANDBOOK OF TRANSPORTATION SCIENCE - 2nd Ed.Glover & Kochenberger/ HANDBOOK OF METAHEURISTICSGraves & Ringuest/ MODELS AND METHODS FOR PROJECT SELEC-

TION: Concepts from Management Science, Finance and InformationTechnology

Hassin & Haviv/ TO QUEUE OR NOT TO QUEUE: Equilibrium Behavior inQueueing Systems

Gershwin et al/ ANALYSIS & MODELING OF MANUFACTURINGSYSTEMS

Maros/ COMPUTATIONAL TECHNIQUES OF THE SIMPLEXMETHOD

Harrison, Lee & Neale/ THE PRACTICE OF SUPPLY CHAIN MANAGE-MENT: Where Theory and Application Converge

Shanthikumar, Yao & Zijm/ STOCHASTICMODELING AND OPTIMIZA-TION OF MANUFACTURING SYSTEMS AND SUPPLY CHAINS

Nabrzyski, Schopf &Weglarz/ GRID RESOURCEMANAGEMENT: State ofthe Art and Future Trends

Thissen & Herder/ CRITICAL INFRASTRUCTURES: State of the Art in R-esearch and Application

Carlsson, Fedrizzi, & Fuller/ FUZZY LOGIC IN MANAGEMENTSoyer, Mazzuchi & Singpurwalla/ MATHEMATICAL RELIABILITY: An

Expository PerspectiveChakravarty & Eliashberg/ MANAGING BUSINESS INTERFACES: Mar-

keting, Engineering, and Manufacturing PerspectivesTalluri & van Ryzin/ THE THEORY AND PRACTICE OF REVENUE

MANAGEMENT

Kavadias & Loch/PROJECT SELECTION UNDER UNCERTAINTY: Dy-namically Allocating Resources to Maximize Value

Brandeau, Sainfort & Pierskalla/ OPERATIONS RESEARCH AND HEAL-TH CARE: A Handbook of Methods and Applications

Cooper, Seiford & Zhu/ HANDBOOK OF DATA ENVELOPMENT ANA-LYSIS: Models and Methods

Luenberger/ LINEAR AND NONLINEAR PROGRAMMING, 2nd Ed.Sherbrooke/ OPTIMAL INVENTORY MODELING OF SYSTEMS: Multi-

Echelon Techniques, Second EditionChu, Leung, Hui & Cheung/ 4th PARTY CYBER LOGISTICS FOR AIR

CARGOSimchi-Levi, Wu & Shen/ HANDBOOK OF QUANTITATIVE SUPPLY C-

HAIN ANALYSIS: Modeling in the E-Business EraGass & Assad/ AN ANNOTATED TIMELINE OF OPERATIONS RESEA-

RCH: An Informal HistoryGreenberg/ TUTORIALS ON EMERGING METHODOLOGIES AND AP-

PLICATIONS IN OPERATIONS RESEARCHWeber/ UNCERTAINTY IN THE ELECTRIC POWER INDUSTRY: Meth-

ods and Models for Decision SupportFigueira, Greco & Ehrgott/ MULTIPLE CRITERIA DECISION ANALY-

SIS: State of the Art SurveysReveliotis/ REAL-TIME MANAGEMENT OF RESOURCE ALLOCA-

TIONS SYSTEMS: A Discrete Event Systems ApproachKall & Mayer/ STOCHASTIC LINEAR PROGRAMMING: Models, Theory,

and ComputationSethi, Yan&Zhang/ INVENTORYANDSUPPLYCHAINMANAGEMENT

WITH FORECAST UPDATESCox/ QUANTITATIVE HEALTH RISK ANALYSIS METHODS: Modeling

the Human Health Impacts of Antibiotics Used in Food AnimalsChing & Ng/ MARKOV CHAINS: Models, Algorithms and ApplicationsLi & Sun/ NONLINEAR INTEGER PROGRAMMINGKaliszewski/ SOFT COMPUTING FOR COMPLEX MULTIPLE CRI-

TERIA DECISION MAKING

* A list of the more recent publications in the series is at the front of the book *