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Retail Supply Chain Management Quantitative Models and Empirical Studies

:	Narendra Agrawal, Stephen A. Smith
:	Narendra Agrawal, Stephen A. Smith
:	Retail Supply Chain Management Quantitative Models and Empirical Studies
:	Springer-Verlag
:	9780387789033
:	1
:	CHF 95.00
:

:	Management
:	English

:	340
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

In today's retail environment, characterized by product proliferation, price competition, expectations of service quality, and advances in technology, many organizations are struggling to maintain profitability. Rigorous analytical methods have emerged as the most promising solution to many of these complex problems. Indeed, the retail industry has emerged as a fascinating choice for researchers in the field of supply chain management.

In Retail Supply Chain Management, leading researchers provide a detailed review of cutting-edge methodologies that address the complex array of these problems. A critical resource for researchers and practitioners in the field of retailing, chapters in this book focus on three key areas: (1) empirical studies of retail supply chain practices, (2) assortment and inventory planning, and (3) integrating price optimization into retail supply chain decisions.

	Foreword	6
	Preface	8
	Contents	10
	Contributors	12
	Overview of Chapters	14
	1.1 Background	14
	1.2 The Focus Of Academic Research In This Volume	15
	REFERENCES	21
	Supply Chain Planning Processes for Two Major Retailers	23
	2.1 Introduction	23
	2.2 Supply Chain Description	25
	2.3 Supply Chain Planning Processes	27
	2.3.1 Product design and assortment planning	29
	2.3.2 Sourcing	29
	30	29
	2.3.3 Logistics planning	31
	2.3.4 Distribution planning and inventory management	32
	2.3.5 Clearance and markdown optimization	33
	2.3.6 Cross-channel optimization	34
	2.4 Conclusion	34
	REFERENCES	35
	The effects of firm size and sales growth rate on inventory turnover performance in the U.S. retail sector	36
	3.1 Introduction	36
	3.2 Literature Review	39
	3.3 Data Description	41
	3.4 Adjusted Inventory Turnover	47
	3.5 Hypotheses	48
	3.5.1 Effect of firm size on inventory turnover	49
	3.5.2 Effect of sales ratio on inventory turnover	51
	3.6 Model	53
	3.7 Results	54
	3.8 Conclusions and Directions for Future Research	59
	REFERENCES	62
	The Role of Execution in Managing Product Availability	64
	4.1 Introduction	64
	4.2 Retail Execution Problems	65
	4.2.1 Inventory record inaccuracy	66
	4.2.2 Misplaced products	67
	4.2.3 Root causes of execution problems	67
	4.3 Factors that Exacerbate Execution Problems	69
	4.3.1 Inventory levels	70
	4.3.2 Product variety	71
	4.3.3 Employee turnover and training	71
	4.3.4 Employee workload	72
	4.3.5 Employee effort	73
	4.4 How Execution Problems Affect Inventory Planning	74
	4.4.1 Effect of inventory record inaccuracy on inventory planning	74
	4.4.2 Effect of misplaced products on inventory planning	75
	4.4.3 Incorporating execution problems into existing research streams	76
	4.5 Future Research Opportunities	77
	4.1 APPENDIX 1	80
	4.1.3 DeHoratius and Raman (2008)	80
	4.1.3 Ton and Raman (2006)	82
	4.1.3 Ton and Raman (2007)	84
	REFERENCES	86
	Category captainship practices in the retail industry	89
	5.1 Introduction	89
	5.1.4 Category captainship implementations in practice	90
	5.2 Review of Existing Research on Category Captainship	92
	5.2.1 Emergence of category captainship	94
	5.2.2 Delegation of the pricing decisions	96
	5.2.3 Delegation of the assortment selection decision	100
	5.2.4 Antitrust concerns	103
	5.3 Impact of Category Captainship Practices on the Retail Industry	104
	5.4 Future Research Directions	105
	REFERENCES	107
	Assortment planning: Review of literature and Industry Practice	109
	6.1 Introduction	109
	6.2 Related Literature	111
	6.2.1 Product variety and product line design	112
	6.2.2 Multi-item inventory models	113
	6.2.3 Shelf space allocation models	114
	6.2.4 Perception of variety	116
	6.3 Demand Models	116
	6.3.1 Consumer driven substitution	117
	6.3.2 Multinomial logit	118
	6.3.3 Exogenous demand model	120
	6.3.4 Locational choice model	123
	6.4 Assortment Selection and Inventory Planning	124
	6.4.1 Assortment planning with multinomial logit: The van Ryzin and Mahajan model	124
	6.4.1.1 Extensions	126
	6.4.2 Assortment planning under exogenous demand models	127
	6.4.2.1 Smith and Agrawal model	128
	6.4.2.2 Kök and Fisher model	129
	6.4.3 Assortment planning under locational choice	133
	6.4.4 Assortment planning in decentralized supply chains	135
	6.4.5 Dynamic assortment planning	136
	6.4.6 Assortment planning models with multiple categories	137
	6.5 Demand Estimation	140
	6.5.1 Estimation of the MNL	140
	6.5.1.1 With panel data	140
	6.5.1.2 With sales transaction data	142
	6.5.1.3 With sales summary data	142
	6.5.2 Estimation of substitution rates in exogenous demand models	145
	6.5.2.1 Estimation of stockout-based substitution	145
	6.5.2.2 Estimation of assortment-based substitution	146
	6.6 Assortment Planning in Practice	147
	6.6.1 Best Buy	147
	6.6.2 Borders	149
	6.6.3 Tanishq	150
	6.6.4 Albert Heijn	153
	6.6.5 Comparison of academic and industry approaches to assortment planning	154
	6.7 Directions for Future Research	156
	REFERENCES	159
	Managing variety on the retail shelf: Using household scanner panel data to rationalize assortments	164
	7.1 Introduction	165
	7.2 Literature Review	167
	7.3 Consumer Model	171
	7.4 The Retailer Assortment and Stocking Problem	174
	7.4.1 Basic formulation	174
	7.4.2 Modeling no purchase	177
	7.4.3 Reformulation	178
	7.4.4 Discussion of the optimization model and some special cases	179
	7.5 Computational Study	180
	7.5.1 Description of household scanner panel data	180
	7.5.2 Solution technique for assortment problem	182
	7.5.3 Optimal assortment	183
	7.6 Summary, Extensions, and Future Work	186
	7.1 Appendix	188
	7.1.3 Proof of Proposition 4.1	188
	REFERENCES	189
	Optimizing Retail Assortments for Diverse Customer Preferences	192
	8.1 Introduction	192
	8.2 Model Description	194
	8.2.1 Modeling the Consumer’s Purchase Decision	195
	8.2.2 Retailer’s assortment optimization	200
	8.2.3 Properties of the optimal assortment	204
	8.2.4 Solving the Optimization Problem	206
	8.3 Illustrative Application for a DVD Player Data Base	207
	8.3.1 Comparing the Model’s Predictions to a