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Assortment Planning: Review of Literature and Industry Practice

  • Chapter
Retail Supply Chain Management

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 223))

Abstract

A retailer’s assortment is defined by the set of products carried in each store at each point in time. The goal of assortment planning is to specify an assortment that maximizes sales or gross margin subject to various constraints, such as a limited budget for purchase of products, limited shelf space for displaying products, and a variety of miscellaneous constraints such as a desire to have at least two vendors for each type of product.

This paper is an invited chapter to appear in Retail Supply Chain Management, Eds. N. Agrawal and S. A. Smith, Kluwer Publishers.

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Notes

  1. 1.

    This hierarchical model of choice is similar to Bucklin and Gupta (1992) that models the first two decisions with an additional focus on the segmentation of customers and Chintagunta (1993) that models all three decisions. Both papers work with household panel data, whereas we work with daily sales data.

  2. 2.

    Albert Heijn, BV is a subsidiary of Ahold Corporation, which owns many supermarket chains around the world with about 8,500 stores and $50 billion in sales.

References

  • AC Nielsen. (1998). Eighth annual survey of trade promotion practices. Chicago, IL: ACNielsen.

    Google Scholar 

  • Agrawal, N., & Smith, S. A. (1996). Estimating negative binomial demand for retail inventory management with unobservable lost sales. Naval Research Logistics, 43, 839–861.

    Google Scholar 

  • Agrawal, N., & Smith, S. A. (2003). Optimal retail assortments for substitutable items purchased in sets. Naval Research Logistics, 50(7), 793–822.

    Google Scholar 

  • Alptekinoǧlu, A. (2004). Mass customization vs. mass production: Variety and price competition. Manufacturing & Service Operations Management, 6(1), 98–103.

    Google Scholar 

  • Alptekinoǧlu, A., & Grasas, A. (2014). When to carry eccentric products? Optimal retail assortment under consumer returns. Production and Operations Management, 23.5, 877–892.

    Google Scholar 

  • Alptekinoǧlu, A., Honhon, D., & Ulu, C. (2012). Positioning and pricing of horizontally differentiated products. Available at SSRN 2166570.

    Google Scholar 

  • Alptekinoǧlu, A., & Semple, J. (2013). The exponomial choice model. Working Paper, Pennsylvania State University.

    Google Scholar 

  • Anderson, S.P., de Palma, A., & Thisse, J. F. (1992). Discrete choice theory of product differentiation. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Anupindi, R., Dada, M., & Gupta, S. (1998). Estimation of consumer demand with stockout based substitution: An application to vending machine products. Marketing Science, 17, 406–423.

    Google Scholar 

  • Avsar, Z. M., & Baykal-Gursoy, M. (2002). Inventory control under substitutable demand: A stochastic game application. Naval Research Logistics, 49, 359–375

    Google Scholar 

  • Aydin, G., & Hausman, W. H. (2003). Supply chain coordination and assortment planning. Working Paper, University of Michigan.

    Google Scholar 

  • Aydin, G., & Ryan, J. K. (2000). Product line selection and pricing under the multinomial logit choice model. In Proceedings of the 2000 MSOM Conference.

    Google Scholar 

  • Bassok, Y., Anupindi, R., & Akella, R. (1999). Single-period multiproduct inventory models with substitution. Operations Research, 47, 632–642.

    Google Scholar 

  • Basuroy, S., & Nguyen, D. (1998). Multinomial logit market share models: Equilibrium characteristics and strategic implications. Management Science, 44(10), 1396–1408.

    Google Scholar 

  • Baumol, W. J., & Ide, E. A. (1956). Variety in retailing. Management Science, 3, 93–101.

    Google Scholar 

  • Bell, D. R., Ho, T.-H., & Tang, C. S. (1998). Determining where to shop: Fixed and variable costs of shopping. Journal of Marketing Research, 35, 352–369.

    Google Scholar 

  • Bell, D. R., & Lattin, J. M. (1998). Shopping behavior and consumer preference for store price format: Why large basket shoppers prefer EDLP. Marketing Science, 17, 66–88.

    Google Scholar 

  • Ben-Akiva, M., & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Bernstein, F., Gürhan Kök, A., & Xie, L. (2011). Dynamic assortment customization with limited inventories. Working Paper, Duke University.

    Google Scholar 

  • Besbes, O., & Saure, D. (2011). Product assortment and price competition with informed consumers. Working Paper, Columbia University.

    Google Scholar 

  • Boatwright, P., & Nunes, J. C. (2001). Reducing assortment: An attribute-based approach. Journal of Marketing, 65(3), 50–63.

    Google Scholar 

  • Borin, N., & Farris, P. (1995). A sensitivity analysis of retailer shelf management models. Journal of Retailing, 71, 153–171.

    Google Scholar 

  • Broniarczyk, S. M., Hoyer, W. D., & McAlister, L. (1998). Consumers’ perception of the assortment offered in a grocery category: The impact of item reduction. Journal of Marketing Research, 35, 166–176.

    Google Scholar 

  • Bucklin, R.E., & Gupta, S. (1992). Brand choice, purchase incidence, and segmentation: An integrated modeling approach. Journal of Marketing Research, 29, 201–215.

    Google Scholar 

  • Bultez, A., & Naert, P. (1988). SHARP: Shelf allocation for retailers profit. Marketing Science, 7, 211–231.

    Google Scholar 

  • Cachon, G. P., & Kök, A. G. (2007). Category management and coordination of categories in retail assortment planning in the presence of basket shoppers. Management Science, 53(6), 934–951.

    Google Scholar 

  • Cachon, G. P., Terwiesch, C., & Xu, Y. (2005). Retail assortment planning in the presence of consumer search. Manufacturing & Service Operations Management, 7(4), 330–346.

    Google Scholar 

  • Cachon, G. P., Terwiesch, C., & Xu, Y. (2008). On the effects of consumer search and firm entry in a multiproduct competitive market. Marketing Science, 27.3, 461–473

    Google Scholar 

  • Campo, K., Gijsbrechts, E., & Nisol, P. (2004). Dynamics in consumer response to product unavailability: Do stock-out reactions signal response to permanent assortment reductions? Journal of Business Research, 57, 834–843.

    Google Scholar 

  • Caro, F., & Gallien, J. (2007). Dynamic assortment with demand learning for seasonal consumer goods. Management Science, 53.2, 276–292.

    Google Scholar 

  • Chen, F., Eliashberg, J., & Zipkin, P. (1998). Customer preferences, supply-chain costs, and product line design. In T.-H. Ho & C. S. Tang (Eds.), Product variety management: Research advances. Norwell: Kluwer Academic Publishers.

    Google Scholar 

  • Chiang, J. (1991). A simultaneous approach to the whether, what and how much to buy questions. Marketing Science, 10, 297–315.

    Google Scholar 

  • Chintagunta, P. K. (1993). Investigating purchase incidence, brand choice and purchase quantity decisions of households. Marketing Science, 12, 184–208.

    Google Scholar 

  • Chong, J. K., Ho, T. H., & Tang, C. S. (2001). A modeling framework for category assortment planning. Manufacturing & Service Operations Management, 3(3), 191–210.

    Google Scholar 

  • Cooper, L. G., & Nakanishi, M. (1988). Market-share analysis: Evaluating competitive marketing effectiveness. Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Corstjens, M., & Doyle, P. (1981). A model for optimizing retail space allocations. Management Science, 27, 822–833.

    Google Scholar 

  • Davis, J. M., Guillermo, G., & Topaloglu, H. (2014). Assortment optimization under variants of the nested logit model. Operations Research, 62(2), 250–273.

    Google Scholar 

  • de Groote, X. (1994). Flexibility and marketing/manufacturing coordination. International Journal of Production Economics, 36, 153–167.

    Google Scholar 

  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39, 1–38.

    Google Scholar 

  • Desai, P., Radhakrishnan, S., & Srinivasan, K. (2001). Product differentiation and commonality in design: Balancing revenue and cost drivers. Management Science, 47, 37–51.

    Google Scholar 

  • DeHoratius, N., & Raman, A. (2008). Inventory record inaccuracy: An empirical analysis. Management Science, 54.4, 627–641.

    Google Scholar 

  • Dhar, S. K., Hoch, S. J., & Kumar, N. (2001). Effective category management depends on the role of the category. Journal of Retailing, 77(2), 165–184.

    Google Scholar 

  • Dobson, G., & Kalish, S. (1993). Heuristics for pricing and positioning a product line. Management Science, 39, 160–175.

    Google Scholar 

  • Downs, B., Metters, R., & Semple, J. (2002). Managing inventory with multiple products, lags in delivery, resource constraints, and lost sales: A mathematical programming approach. Management Science, 47, 464–479.

    Google Scholar 

  • Dreze, X., Hoch, S. J., & Purk, M. E. (1994). Shelf management and space elasticity. Journal of Retailing, 70, 301–326.

    Google Scholar 

  • Eliashberg, J., & Steinberg, R. (1993). Marketing-production joint decision-making. In J. Eliashberg & G. L. Lilien (Eds.), Handbooks in OR & MS (Vol. 5). Amsterdam: Elsevier

    Google Scholar 

  • Emmelhainz, L., Emmelhainz, M., & Stock, J. (1991). Logistics implications of retail stockouts. Journal of Business Logistics, 12(2), 129–141.

    Google Scholar 

  • Fader, P. S., & Hardie, B. G. S. (1996). Modeling consumer choice among SKUs. Journal of Marketing Research, 33, 442–452.

    Google Scholar 

  • Farias, V. F., Jagabathula, S., & Shah, D. (2013). A nonparametric approach to modeling choice with limited data. Management Science, 59.2, 305–322.

    Google Scholar 

  • Fisher, M. L., & Raman, A. (1996). Reducing the cost of demand uncertainty through accurate response to early sales. Operations Research, 44, 87–99.

    Google Scholar 

  • Fisher, M., & Vaidyanathan, R. (2014). A demand estimation procedure for retail assortment optimization with results from implementations. Management Science 60(10), 2401–2415.

    Google Scholar 

  • Gaur, V., & Honhon, D. (2006). Assortment planning and inventory decisions under a locational choice model. Management Science, 52(10), 1528–1543.

    Google Scholar 

  • Greene, W. H. (1997). Econometric analysis. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Gruca, T. S., & Sudharshan, D. (1991). Equilibrium characteristics of multinomial logit market share models. Journal of Marketing Research, 28(11), 480–482.

    Google Scholar 

  • Gruen, T. W., Corsten, D. S., & Bharadwaj, S. (2002). Retail out-of-stocks: A worldwide examination of extent, causes and consumer responses. Grocery Manufacturers of America.

    Google Scholar 

  • Guadagni, P. M., & Little, J. D. C. (1983). A logit model of brand choice calibrated on scanner data. Marketing Science, 2, 203–238.

    Google Scholar 

  • Hadley, G., & Whitin, T. M. (1963). Analysis of inventory systems. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Hoch, S. J., Bradlow, E. T., & Wansink, B. (1999). The variety of an assortment. Marketing Science, 18(4), 527–546.

    Google Scholar 

  • Honhon, D., Gaur, V., & Seshadri, S. (2010). Assortment planning and inventory decisions under stockout-based substitution. Operations Research, 58.5, 1364–1379.

    Google Scholar 

  • Honhon, D., Jonnalagedda, S., & Pan, X. A. (2012) Optimal algorithms for assortment selection under ranking-based consumer choice models. Manufacturing & Service Operations Management, 14.2, 279–289.

    Google Scholar 

  • Hopp, W. J., & Xu, X. (2008). A static approximation for dynamic demand substitution with applications in a competitive market. Operations Research, 56.3, 630–645.

    Google Scholar 

  • Hotelling, H. (1929). Stability in competition. Economic Journal, 39, 41–57

    Google Scholar 

  • Huffman, C., & Kahn, B. E. (1998). Variety for sale: Mass customization or mass confusion? Journal of Retailing, 74, 491–513.

    Google Scholar 

  • Irion, J., Al-Khayyal, F., & Lu, J. (2012). A piecewise linearization framework for retail shelf space management models. European Journal of Operational Research, 222(1), 122–136.

    Google Scholar 

  • Jain, A., Rudi, N., & Wang, T. (2014). Demand estimation and ordering under censoring: Stock–out timing is (almost) all you need. Operations Research, 63(1), 134–150.

    Google Scholar 

  • Kahn, B. E. (1995). Consumer variety-seeking in goods and services: An integrative review. Journal of Retailing and Consumer Services, 2, 139–48.

    Google Scholar 

  • Kohli, R., & Sukumar, R. (1990). Heuristics for product line design. Management Science, 36(3), 1464–1478.

    Google Scholar 

  • Kök, A. G. (2003). Management of product variety in retail operations. Ph.D. Dissertation, The Wharton School, University of Pennsylvania.

    Google Scholar 

  • Kök, A. G., & Fisher, M. L. (2007). Demand estimation and assortment optimization under substitution: Methodology and application. Operations Research, 55(6), 1001–1021.

    Google Scholar 

  • Kök, A. G., & Xu, Y. (2011). Optimal and competitive assortments with endogenous pricing under hierarchical consumer choice models. Management Science, 57.9, 1546–1563.

    Google Scholar 

  • Kök, A., & Martínez-de-Albéniz, V. (2013). A Competitive Model for Quick–Response Product Decisions. Working Paper, Duke University.

    Google Scholar 

  • Kurt Salmon Associates. (1993). Efficient consumer response: Enhancing consumer value in the grocery industry. Food Marketing Institute Report # 9–526, Food Marketing Institute.

    Google Scholar 

  • Kurtulus, M. (2005). Supply chain collaboration practices in consumer goods industry. Ph.D. Dissertation, INSEAD.

    Google Scholar 

  • Kurtulus, M., & Toktay, B. (2007). Category captainship: Outsourcing retail category management. Working Paper, Vanderbilt University.

    Google Scholar 

  • Lancaster, K. (1966). A new approach to consumer theory. Journal of Political Economy, 74, 132–57.

    Google Scholar 

  • Lancaster, K. (1975). Socially optimal product differentiation. American Economic Review, 65, 567–585.

    Google Scholar 

  • Lancaster, K. (1990). The economics of product variety: A survey. Marketing Science, 9, 189–210.

    Google Scholar 

  • Levy, M., & Weitz, B. A. (2004). Retailing management (pp. 398–400). New York: McGraw-Hill/Irwin.

    Google Scholar 

  • Li, Z. (2007). A single-period assortment optimization model. Production and Operations Management, 16.3, 369–380.

    Google Scholar 

  • Lippman S. A., & McCardle, K. F. (1997). The competitive newsboy. Operations Research, 45, 54–65.

    Google Scholar 

  • Maddah, B., & Bish, E. K. (2004). Joint pricing, assortment, and inventory decisions for a retailer’s product line. 2007. Naval Research Logistics, 54(3), 315–330.

    Google Scholar 

  • Mahajan, S., & van Ryzin, G. J. (1999). Retail inventories and consumer choice. Chapter 17. In S. Tayur, et al. (Eds.), Quantitative methods in supply chain management. Amsterdam: Kluwer.

    Google Scholar 

  • Mahajan, S., & van Ryzin, G. (2001a). Stocking retail assortments under dynamic consumer substitution. Operations Research, 49(3), 334–351.

    Google Scholar 

  • Mahajan, S., & van Ryzin, G. (2001b). Inventory competition under dynamic consumer choice. Operations Research, 49(5), 646–657.

    Google Scholar 

  • Manchanda, P., Ansari, A., & Gupta, S. (1999). The “shopping basket”: A model for multicategory purchase incidence decisions. Marketing Science, 18(2), 95–114.

    Google Scholar 

  • Martínez-de-Albéniz, V., & Roels, G. (2011). Competing for shelf space. Production and Operations Management 20(1), 32–46.

    Google Scholar 

  • McBride, R. D., & Zufryden, F. S. (1988). An integer programming approach to the optimal product line selection problem. Marketing Science, 7(2), 126–140.

    Google Scholar 

  • McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics. New York: Academic.

    Google Scholar 

  • McGillivray, A. R., & Silver, E. A. (1978). Some concepts for inventory control under substitutable demand. INFOR, 16, 47–63.

    Google Scholar 

  • Miller, C. M., Smith, S. A., McIntyre, S. H., & Achabal, D. D. (2010). Optimizing retail assortments for infrequently purchased products. Journal of Retailing, 86(2), 159–171

    Google Scholar 

  • Miranda Bront, J., Mendez-Diaz, I., & Vulcano, G. (2009). A column generation algorithm for choice-based network revenue management. Operations Research, 57(3), 769–784.

    Google Scholar 

  • Moorthy, S. (1984). Market segmentation, self-selection, and product line design. Marketing Science, 3, 288–307.

    Google Scholar 

  • Musalem, A., et al. (2010). Structural estimation of the effect of out-of-stocks. Management Science, 56.7, 1180–1197.

    Google Scholar 

  • Mussa, M., & Rosen, S. (1978). Monopoly and product quality. Journal of Economic Theory, 18, 301–317.

    Google Scholar 

  • Netessine, S., & Rudi, N. (2003). Centralized and competitive inventory models with demand substitution. Operations Research, 51, 329–335.

    Google Scholar 

  • Netessine, S., & Taylor, T. A. (2007). Product line design and production technology. Marketing Science, 26(1), 101–117.

    Google Scholar 

  • Noonan, P. S. (1995). When consumers choose: A multi-product, multi-location newsboy model with substitution. Working Paper, Emory University.

    Google Scholar 

  • Pan, X. A., & Honhon, D. (2012). Assortment planning for vertically differentiated products. Production and Operations Management, 21.2, 253–275.

    Google Scholar 

  • Parlar, M. (1985). Optimal ordering policies for a perishable and substitutable product: A Markov decision model. Infor, 23, 182–195.

    Google Scholar 

  • Parlar, M., & Goyal, S. K. (1984). Optimal ordering policies for two substitutable products with stochastic demand. Opsearch, 21(1), 1–15.

    Google Scholar 

  • Progressive Grocer. (1968a, October). The out of stock study: Part I. S1–S16.

    Google Scholar 

  • Progressive Grocer. (1968b, November). The out of stock study: Part II. S17–S32.

    Google Scholar 

  • Quelch, J. A., & Kenny, D. (1994). Extend profits, not product lines. Harvard Business Review, 72, 153–160.

    Google Scholar 

  • Rajaram, K. (2001). Assortment planning in fashion retailing: Methodology, application and analysis. European Journal of Operational Research, 129, 186–208.

    Google Scholar 

  • Rajaram, K., & Tang, C. S. (2001). The impact of product substitution on retail merchandising. European Journal of Operational Research, 135, 582–601.

    Google Scholar 

  • Raman, A., McClellan, A. d., & Fisher, M. L. (2001). Supply chain management at world Co. Ltd. Harvard Business School Case # 601072.

    Google Scholar 

  • Rusmevichientong, P., & Topaloglu, H. (2012). Robust assortment optimization in revenue management under the multinomial logit choice model. Operations Research, 60.4, 865–882.

    Google Scholar 

  • Rusmevichientong, P., Shen, Z.-J. M., & Shmoys, D. B. (2010). Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Operations Research, 58.6, 1666–1680.

    Google Scholar 

  • Russell, G. J., Bell, D. R., et al. (1997). Perspectives on multiple category choice. Marketing Letters, 8(3), 297–305.

    Google Scholar 

  • Saure, D., & Zeevi, A. (2013). Optimal dynamic assortment planning with demand learning. Manufacturing & Service Operations Management, 15(3), 387–404.

    Google Scholar 

  • Schary, P., & Christopher, M. (1979). The anatomy of a stockout. Journal of Retailing, 55(2), 59–70.

    Google Scholar 

  • Simonson, I. (1999). The effect of product assortment on buyer preferences. Journal of Retailing, 75, 347–370.

    Google Scholar 

  • Singh, P., Groenevelt, H., & Rudi, N. (2005). Product variety and supply chain structures. Working Paper, University of Rochester.

    Google Scholar 

  • Smith, S. A., & Agrawal, N. (2000). Management of multi-item retail inventory systems with demand substitution. Operations Research, 48, 50–64.

    Google Scholar 

  • Song, J.-S. (1998). On the order fill rate in multi-item, base-stock systems. Operations Research, 46, 831–845.

    Google Scholar 

  • Song, J.-S., & Zipkin, P. (2003). Supply chain operations: Assemble-to-order systems. In S. Graves & T. De Kok (Eds.), Handbooks in operations research and management science. Supply chain management (Vol. XXX). North-Holland: Amsterdam.

    Google Scholar 

  • Talluri, K., & van Ryzin, G. (2004). Revenue management under a general discrete choice model of consumer behavior. Management Science, 50, 15–33.

    Google Scholar 

  • Ulu, C., Honhon, D., & Alptekinoǧlu, A. (2012). Learning consumer tastes through dynamic assortments. Operations Research, 60.4, 833–849.

    Google Scholar 

  • Urban, T. L. (1998). An inventory-theoretic approach to product assortment and shelf space allocation. Journal of Retailing, 74, 15–35.

    Google Scholar 

  • Vaidyanathan, R., & Fisher, M. (2012). Assortment planning. Working Paper, The Wharton School, University of Pennsylvania.

    Google Scholar 

  • van Herpen, E., & Pieters, R. (2002). The variety of an assortment: An extension to the attribute-based approach. Marketing Science, 21(3), 331–341.

    Google Scholar 

  • van Ryzin, G., & Mahajan, S. (1999). On the relationship between inventory costs and variety benefits in retail assortments. Management Science, 45, 1496–1509.

    Google Scholar 

  • van Ryzin, G., & Vulcano, G. (2013). An expectation-maximization algorithm to estimate a general class of non-parametric choice-models. Working Paper.

    Google Scholar 

  • Vulcano, G., Van Ryzin, G., & Ratliff, R. (2012). Estimating primary demand for substitutable products from sales transaction data. Operations Research, 60.2, 313–334.

    Google Scholar 

  • Walter, C., & Grabner, J. (1975). Stockout models: Empirical tests in a retail situation. Journal of Marketing, 39, 56–68.

    Google Scholar 

  • Wu, C. F. J. (1983). On the convergence properties of the EM algorithm. Annals of Statistics, 11, 95–103.

    Google Scholar 

  • Zinn, W., & Liu, P. (2001). Consumer response to retail stockouts. Journal of Business Logistics, 22(1), 49–71.

    Google Scholar 

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Authors and Affiliations

  1. College of Administrative Sciences and Economics, Koç University, Istanbul, Turkey

    A. Gürhan Kök

  2. The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Marshall L. Fisher

  3. Desautels Faculty of Management, McGill University, Montréal, QC, Canada

    Ramnath Vaidyanathan

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Correspondence to Ramnath Vaidyanathan .

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  1. Department of Operations and Management and Information Systems, Leavey School of Business, Santa Clara University, Santa Clara, California, USA

    Narendra Agrawal

  2. Department of Operations and Management and Information Systems, Leavey School of Business, Santa Clara University, Santa Clara, California, USA

    Stephen A. Smith

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Kök, A.G., Fisher, M.L., Vaidyanathan, R. (2015). Assortment Planning: Review of Literature and Industry Practice. In: Agrawal, N., Smith, S. (eds) Retail Supply Chain Management. International Series in Operations Research & Management Science, vol 223. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7562-1_8

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