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- 01 Apr 2024
- In Practice
Navigating the Mood of Customers Weary of Price Hikes
Price increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium.
- 17 Jan 2024
Psychological Pricing Tactics to Fight the Inflation Blues
Inflation has slowed from the epic rates of 2021 and 2022, but many consumers still feel pinched. What will it take to encourage them to spend? Thoughtful pricing strategies that empower customers as they make purchasing decisions, says research by Elie Ofek.
- 05 Dec 2023
- Cold Call Podcast
Tommy Hilfiger’s Adaptive Clothing Line: Making Fashion Inclusive
In 2017, Tommy Hilfiger launched its adaptive fashion line to provide fashion apparel that aims to make dressing easier. By 2020, it was still a relatively unknown line in the U.S. and the Tommy Hilfiger team was continuing to learn more about how to serve these new customers. Should the team make adaptive clothing available beyond the U.S., or is a global expansion premature? Assistant Professor Elizabeth Keenan discusses the opportunities and challenges that accompanied the introduction of a new product line that effectively serves an entirely new customer while simultaneously starting a movement to provide fashion for all in the case, “Tommy Hilfiger Adaptive: Fashion for All.”
- 05 Jul 2023
How Unilever Is Preparing for the Future of Work
Launched in 2016, Unilever’s Future of Work initiative aimed to accelerate the speed of change throughout the organization and prepare its workforce for a digitalized and highly automated era. But despite its success over the last three years, the program still faces significant challenges in its implementation. How should Unilever, one of the world's largest consumer goods companies, best prepare and upscale its workforce for the future? How should Unilever adapt and accelerate the speed of change throughout the organization? Is it even possible to lead a systematic, agile workforce transformation across several geographies while accounting for local context? Harvard Business School professor and faculty co-chair of the Managing the Future of Work Project William Kerr and Patrick Hull, Unilever’s vice president of global learning and future of work, discuss how rapid advances in artificial intelligence, machine learning, and automation are changing the nature of work in the case, “Unilever's Response to the Future of Work.”
- 25 Apr 2023
How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model
The platforms SHEIN and Temu match consumer demand and factory output, bringing Chinese production to the rest of the world. The companies have remade fast fashion, but their pioneering approach has the potential to go far beyond retail, says John Deighton.
- 11 Apr 2023
- Research & Ideas
Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide
More companies are bringing seemingly unrelated businesses together in new ways, challenging traditional stock categories. MarcAntonio Awada and Suraj Srinivasan discuss how applying machine learning to regulatory data could reveal new opportunities for investors.
- 04 Apr 2023
Two Centuries of Business Leaders Who Took a Stand on Social Issues
Executives going back to George Cadbury and J. N. Tata have been trying to improve life for their workers and communities, according to the book Deeply Responsible Business: A Global History of Values-Driven Leadership by Geoffrey Jones. He highlights three practices that deeply responsible companies share.
- 03 Mar 2023
When Showing Know-How Backfires for Women Managers
Women managers might think they need to roll up their sleeves and work alongside their teams to show their mettle. But research by Alexandra Feldberg shows how this strategy can work against them. How can employers provide more support?
- 27 Feb 2023
How One Late Employee Can Hurt Your Business: Data from 25 Million Timecards
Employees who clock in a few minutes late—or not at all—often dampen sales and productivity, says a study of 100,000 workers by Ananth Raman and Caleb Kwon. What can managers do to address chronic tardiness and absenteeism?
- 06 Dec 2022
Latest Isn’t Always Greatest: Why Product Updates Capture Consumers
Consumers can't pass up a product update—even if there's no improvement. Research by Leslie John, Michael Norton, and Ximena Garcia-Rada illustrates the powerful allure of change. Are we really that naïve?
- 29 Nov 2022
How Much More Would Holiday Shoppers Pay to Wear Something Rare?
Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?
- 21 Nov 2022
Buy Now, Pay Later: How Retail's Hot Feature Hurts Low-Income Shoppers
More consumers may opt to "buy now, pay later" this holiday season, but what happens if they can't make that last payment? Research by Marco Di Maggio and Emily Williams highlights the risks of these financing services, especially for lower-income shoppers.
- 18 Oct 2022
Chewy.com’s Make-or-Break Logistics Dilemma
In late 2013, Ryan Cohen, cofounder and then-CEO of online pet products retailer Chewy.com, was facing a decision that could determine his company’s future. Should he stay with a third-party logistics provider (3PL) for all of Chewy.com’s e-commerce fulfillment or take that function in house? Cohen was convinced that achieving scale would be essential to making the business work and he worried that the company’s current 3PL may not be able to scale with Chewy.com’s projected growth or maintain the company’s performance standards for service quality and fulfillment. But neither he nor his cofounders had any experience managing logistics, and the company’s board members were pressuring him to leave order fulfillment to the 3PL. They worried that any changes could destabilize the existing 3PL relationship and endanger the viability of the fast-growing business. What should Cohen do? Senior Lecturer Jeffrey Rayport discusses the options in his case, “Chewy.com (A).”
- 06 Sep 2022
Reinventing an Iconic Independent Bookstore
In 2020, Kwame Spearman (MBA 2011) made the career-shifting decision to leave a New York City-based consulting job to return to his hometown of Denver, Colorado, and take over an iconic independent bookstore, The Tattered Cover. Spearman saw an opportunity to reinvent the local business to build a sense of community after the pandemic. But he also had to find a way to meet the big challenges facing independent booksellers amid technological change and shifting business models. Professor Ryan Raffaelli and Spearman discuss Spearman’s vision for reinventing The Tattered Cover, as well as larger insights around how local businesses can successfully compete with online and big box retailers in the case, “Kwame Spearman at Tattered Cover: Reinventing Brick-and-Mortar Retail.”
- 26 Jul 2022
Burgers with Bugs? What Happens When Restaurants Ignore Online Reviews
Negative Yelp reviews hold more sway with consumers than restaurateurs might think. A machine learning study by Chiara Farronato reveals how online platforms amplify the customer voice, and why business owners should listen.
- 22 Mar 2022
How Etsy Found Its Purpose and Crafted a Turnaround
Etsy, the online seller of handmade goods, was founded in 2005 as an alternative to companies that sold mass-manufactured products. The company grew substantially, but remained unprofitable under the leadership of two early CEOs. Ten years later, Etsy went public and was forced into a new arena, where it was beholden to stakeholders who demanded financial success and accountability. Unable to contain costs, the company was almost bought out by private equity firms in 2017—until CEO Josh Silverman arrived with a mission to save the company financially and, in the process, save its soul. Harvard Business School professor Ranjay Gulati discusses the purpose-driven turnaround Silverman and his team led at Etsy—to make the company profitable and improve its social and environmental impact—in the case, “Etsy: Crafting a Turnaround to Save the Business and Its Soul.” Open for comment; 0 Comments.
- 05 Nov 2021
Is the Business World Finally Ready for the Wisdom of Shibusawa?
Legendary financier Eiichi Shibusawa advocated for business prosperity that would also benefit society. One hundred years after his death, his message is resonating with a new generation of leaders, say Geoffrey Jones and Rei Morimoto. Open for comment; 0 Comments.
- 19 Oct 2021
Fed Up Workers and Supply Woes: What's Next for Dollar Stores?
Willy Shih discusses how higher costs, shipping delays, and worker shortages are putting the dollar store business model to the test ahead of the critical holiday shopping season. Open for comment; 0 Comments.
- 13 Jul 2021
Strategies for Underdogs: How Alibaba’s Taobao Beat eBay in China
In 2007, Alibaba’s Taobao became China’s leading consumer e-commerce marketplace, displacing the once dominant eBay. How did underdog Taobao do it? And will it be able to find a way to monetize its marketplace and ensure future success? Professor Felix Oberholzer-Gee discusses his case, “Alibaba’s Taobao,” and related strategy lessons from his new book, Better, Simpler Strategy: A Value-Based Guide to Exceptional Performance. Open for comment; 0 Comments.
- 01 Jul 2021
- Office Hours
Readers Ask: Which Companies Are Transforming Work?
Joseph Fuller answers readers' questions about automation, virtual internships, and the future of work on Working Knowledge’s “Office Hours” series. Open for comment; 0 Comments.
Retail supply chain management: a review of theories and practices
- Original Article
- Published: 18 July 2019
- Volume 1 , pages 45–64, ( 2019 )
Cite this article
- Deng Ge 1 ,
- Zuo-Jun (Max) Shen 1 , 2 ,
- Di Wu ORCID: orcid.org/0000-0002-2895-3407 1 ,
- Rong Yuan 1 &
- Chao Zhang 1
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Retail business has been rapidly evolving in the past decades with the boom of internet, mobile technologies and most importantly e-commerce. Supply chain management, as a core part of retail business, has also gone through significant changes with new business scenarios and more advanced technologies in both algorithm design and computation power. In this review, we focus on several core components of supply chain management, i.e. vendor management, demand forecasting, inventory management and order fulfillment. We will discuss the key innovations from both academia and industry and highlight the current trend and future challenges.
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An Overview of Decisions, Performance and Analytics in Supply Chain Management
Overview of Chapters
Supply Chain Management: An Overview
https://jdata.joybuy.com/en/html/detail.html?id=4
Acimovic J, Graves SC (2017) Mitigating spillover in online retailing via replenishment. Manuf Serv Oper Manag 19(3):419–436
Google Scholar
Agatz NA, Fleischmann M, Van Nunen JA (2008) E-fulfillment and multi-channel distribution–a review. Eur J Oper Res 187(2):339–356
MATH Google Scholar
Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. Econ Rev 29(5-6):594–621
MathSciNet Google Scholar
Allen SG (1958) Redistribution of total stock over several user locations. Naval Research Logistics Quarterly 5(4):337–345
Allen SG (1961) A redistribution model with set-up charge. Manag Sci 8(1):99–108
MathSciNet MATH Google Scholar
Amazon.com (2018) Amazon’s Fulfillment Network. https://www.aboutamazon.com/working-at-amazon/amazons-fulfillment-network (accessed November 19, 2018)
Andraski JC, Haedicke J (2003) CPFR: time For the breakthrough? Supply Chain Management Review 7 (3):54–60
Angulo A, Nachtmann H, Waller MA (2004) Supply chain information sharing in a vendor managed inventory partnership. J Bus Logist 25(1):101–120
Anupindi R, Dada M, Gupta S (1998) Estimation of consumer demand with stock-out based substitution: an application to vending machine products. Mark Sci 17(4):406–423
Archibald TW, Black D, Glazebrook KD (2009) An index heuristic for transshipment decisions in multi-location inventory systems based on a pairwise decomposition. Eur J Oper Res 192(1):69–78
Axsäter S (2003) A new decision rule for lateral transshipments in inventory systems. Manag Sci 49 (9):1168–1179
Bagchi S, Buckley SJ, Ettl M, Lin GY (1998) Experience using the IBM supply chain simulator. In: Simulation Conference Proceedings, 1998. Winter, vol 2. IEEE, pp 1387–1394
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv: 14090473
Barkan O, Koenigstein N (2016) Item2vec: neural item embedding for collaborative filtering. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, pp 1–6
Bass FM (1969) A new product growth for model consumer durables. Manag Sci 15(5):215–227
Bernstein F, Kök AG, Xie L (2015) Dynamic assortment customization with limited inventories. Manuf Serv Oper Manag 17(4):538–553
Bianchi FM, Maiorino E, Kampffmeyer MC, Rizzi A, Jenssen R (2017) An overview and comparative analysis of recurrent neural networks for short term load forecasting. arXiv: 170504378
Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31(3):307–327
Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time Series Analysis: Forecasting and Control. Wiley, Hoboken
Brahimi N, Dauzere-Peres S, Najid NM, Nordli A (2006) Single item lot sizing problems. Eur J Oper Res 168(1):1–16
Brandolese A, Cigolini R (1999) A new model for the strategic management of inventories subject to peaks in market demand. Int J Prod Res 37(8):1859–1880
Breiman L (2017) Classification and regression trees. Routledge
Cachon GP, Fisher M (2000) Supply chain inventory management and the value of shared information. Manag Sci 46(8):1032–1048
Cachon GP, Zipkin PH (1999) Competitive and cooperative inventory policies in a two-stage supply chain. Manag Sci 45(7):936–953
Caro F, Gallien J (2007) Dynamic assortment with demand learning for seasonal consumer goods. Manag Sci 53(2):276–292
Cattani KD, Souza GC (2002) Inventory rationing and shipment flexibility alternatives for direct market firms. Prod Oper Manag 11(4):441–457
Çetinkaya S, Lee CY (2000) Stock replenishment and shipment scheduling for vendor-managed inventory systems. Manag Sci 46(2):217–232
Chaharsooghi SK, Heydari J, Zegordi SH (2008) A reinforcement learning model for supply chain ordering management: an application to the beer game. Decis Support Syst 45(4):949–959
Chen C (2018a) Amazon now sells 76 of its own private-label brands — from clothes to baby wipes. https://www.businessinsider.com/amazon-private-label-brands-list-2018-4 (accessed December 3, 2018)
Chen C (2018b) Online retailer JD.com follows Amazon’s footsteps with own brand selling household basics. https://www.scmp.com/tech/china-tech/article/2128481/online-retailer-jdcom-follows-amazons-footsteps-own-brand-selling (accessed December 3, 2018)
Chen F, Song JS (2001) Optimal policies for multiechelon inventory problems with markov-modulated demand. Oper Res 49(2):226–234
Chen F, Drezner Z, Ryan JK, Simchi-Levi D (2000) Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Manag Sci 46(3):436–443
Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 785–794
Chen X, Ma W, Simchi-Levi D, Xin L (2016) Assortment planning for recommendations at checkout under inventory constraints, available at SSRN: https://ssrn.com/abstract=2853093
Chiles CR, Dau MT (2005) An analysis of current supply chain best practices in the retail industry with case studies of wal-mart and amazon.com. Master’s thesis, Massachusetts Institute of Technology
Choudhury SR (2018) Walmart and JD.com invest $500 million in a Chinese online delivery company. https://www.cnbc.com/2018/08/09/jdcom-walmart-invest-500-million-in-chinese-company-dada-jd-daojia.html (accessed November 30, 2018)
Cinar YG, Mirisaee H, Goswami P, Gaussier E, Aït-Bachir A, Strijov V (2017) Position-based content attention for time series forecasting with sequence-to-sequence RNNs. In: International Conference on Neural Information Processing, Springer, pp 533–544
Clark AJ, Scarf H (1960) Optimal policies for a multi-echelon inventory problem. Manag Sci 6(4):475–490
Conlon CT, Mortimer JH (2013) Demand estimation under incomplete product availability. American Economic Journal:, Microeconomics 5(4):1–30
Cornnejols G, Fisher M, Nemhauser G (1977) Location of bank accounts of optimize float: an analytic study of exact and approximate algorithm. Manag Sci 23(8):789–810
Covert RP, Philip GC (1973) An EOQ model for items with Weibull distribution deterioration. AIIE Transactions 5(4):323–326
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B Methodol 39(1):1–38
DeValve L, Wei Y, Wu D, Yuan R (2018) Understanding the value of fulfillment flexibility in an online retailing environment, available at SSRN: http://ssrn.com/abstract=3265838
Doganis P, Alexandridis A, Patrinos P, Sarimveis H (2006) Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. J Food Eng 75(2):196–204
Drucker H, Burges CJ, Kaufman L, Smola AJ, Vapnik V (1997) Support vector regression machines. In: Advances in Neural Information Processing Systems, pp 155–161
Duan Y, Yao Y, Huo J (2015) Bullwhip effect under substitute products. J Oper Manag 36:75–89
Dudek G (2015) Short-term load forecasting using random forests. In: Intelligent Systems’ 2014, Springer, pp 821–828
Elmaghraby W, Keskinocak P (2003) Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions. Manag Sci 49(10):1287–1309
Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica:, Journal of the Econometric Society pp 987–1007
Estes R (1973) The joint probability approach and reorder point determination. Journal of Production and Inventory Management 14(2):50–56
Fan C, Zhang Y, Pan Y, Li X, Zhang C, Yuan R, Wu D, Wang W, Pei J, Huang H (2019) Multi-horizon time series forecasting with temporal attention learning. In: 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, USA
Fan Y, Li P, Song Z (2006) Dynamic least squares support vector machine. In: 2006 6Th World Congress on Intelligent Control and Automation, vol 1. IEEE, pp 4886–4889
Federgruen A, Zipkin P (1984) Computational issues in an infinite-horizon, multiechelon inventory model. Oper Res 32(4):818–836
Flunkert V, Salinas D, Gasthaus J (2017) DeepAR: Probabilistic forecasting with autoregressive recurrent networks. arXiv: 170404110
Forrester JW (1958) Industrial dynamics. a major breakthrough for decision makers. Harv Bus Rev 36(4):37–66
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat pp 1189–1232
Gallego G, Iyengar G, Phillips R, Dubey A (2004) CORC Technical Report TR-2004-01. Managing flexible products on a network
Gendron B (2011) Decomposition methods for network design. Procedia Soc Behav Sci 20:31–37
Gendron B, Larose M (2014) Branch-and-price-and-cut for large-scale multicommodity capacitated fixed-charge network design. EURO Journal on Computational Optimization 2(1-2):55–75
Girard A, Rasmussen CE, Candela JQ, Murray-Smith R (2003) Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting. In: Advances in Neural Information Processing Systems, pp 545–552
Govindarajan A, Sinha A, Uichanco J (2018) Joint inventory and ful fillment decisions for omnichannel retail networks, available at SSRN: http://ssrn.com/abstract=2924850
Graves SC, Willems SP (2000) Optimizing strategic safety stock placement in supply chains. Manuf Serv Oper Manag 2(1):68–83
Guide VDR Jr, Srivastava R (2000) A review of techniques for buffering against uncertainty with MRP systems. Prod Plan Control 11(3):223–233
Hamilton JD (1995) Time Series Analysis. Economic Theory II. Princeton University Press, USA, pp 625–630
Hamzaçebi C (2008) Improving artificial neural networks’ performance in seasonal time series forecasting. Inf Sci 178(23):4550–4559
Harris FW (1915) Operations and cost. Factory Management Series pp 48–52
Hastie T, Tibshirani R (1996) Discriminant adaptive nearest neighbor classification and regression. In: Advances in Neural Information Processing Systems, pp 409–415
Herer YT, Tzur M, Yücesan E (2002) Transshipments: an emerging inventory recourse to achieve supply chain leagility. Int J Prod Econ 80(3):201–212
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast 20(1):5–10
van Houtum GJ, Zijm WHM (1991) Computational procedures for stochastic multi-echelon production systems. Int J Prod Econ 23(1-3):223–237
Hübner A, Holzapfel A, Kuhn H (2016) Distribution systems in omni-channel retailing. Bus Res 9 (2):255–296
Ismail Z, Abu N (2013) A study on new product demand forecasting based on bass diffusion model. J Math Stat 9(2):84
Jain A, Rudi N, Wang T (2014) Demand estimation and ordering under censoring: stock-out timing is (almost) all you need. Oper Res 63(1):134–150
Jakob K, Pruzan PM (1983) The simple plant location problem: survey and synthesis. Eur J Oper Res 12:36–81
Jasin S, Sinha A (2015) An LP-based correlated rounding scheme for multi-item ecommerce order fulfillment. Oper Res 63(6):1336–1351
JDA Software (2018) JDA Introduces Luminate Demand Edge to Power Self-Learning, Autonomous Demand Forecasts. https://jda.com/knowledge-center/press-release/jda-introduces-luminate-demand-edge-to-power-selflearning-autonomous-demand-forecasts (accessed May 15, 2019)
JD.com (2018a) JD.com Business. https://corporate.jd.com/ourBusiness (accessed November 19, 2018)
JD.com (2018b) JD.com Inc 3Q2018 Financial and Operational Highlights. http://ir.jd.com/static-files/e7ae12fc-41b9-43c9-9ddb-e6afe1155f11 (accessed November 19, 2018)
Jehl T, Shi Y, Wu D, Shen ZJM (2018) Product placement optimization using parametric cuts, working paper
Jordan WC, Graves SC (1995) Principles on the benefits of manufacturing process flexibility. Manag Sci 41(4):577–594
Kahn KB (2014) New product forecasting: an applied approach. Routledge
Kam HT (1995) Random decision forest. In: Proc of the 3rd int’l conf on document analysis and recognition, Montreal, Canada, August, pp 14–18
Kamruzzaman J, Sarker RA (2006) Artificial neural networks: applications in finance and manufacturing. In: Artificial Neural Networks in Finance and Manufacturing, IGI Global, pp 1–27
Kaplan A (1969) Stock rationing. Manag Sci 15(5):260–267
Karkazis J, Boffey T (1981) The multi-commodity facilities location problem. J Oper Res Soc 32(9):803–814
Karmarkar US (1981) The multiperiod multilocation inventory problem. Oper Res 29(2):215–228
Karp J (2017) Models and methods for omni-channel fulfillment. PhD thesis, Carnegie Mellon University
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) Lightgbm: A highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp 3146–3154
Kihoro JM, Otieno RO, Wafula C (2004) Seasonal time series forecasting: a comparative study of ARIMA and ANN models. Afr J Sci Technol 5(2):41–49
Kim SM, Mahoney JT (2010) Collaborative planning, forecasting and replenishment (CPFR) as a relational contract: an incomplete contracting perspective. Int J Learn Intellect Cap 7(3-4):403–428
Koenker R, Bassett G Jr (1978) Regression quantiles. Econometrica: Journal of the Econometric Society 46:33–50
Kök AG, Fisher ML, Vaidyanathan R (2008) Assortment planning: Review of literature and industry practice. In: Retail Supply Chain Management, Springer, pp 99–153
de Kok T, Janssen F, Van Doremalen J, Van Wachem E, Clerkx M, Peeters W (2005) Philips electronics synchronizes its supply chain to end the bullwhip effect. Interfaces 35(1):37–48
Kosasi S, Saragih H, Kom M (2014) How RFID technology boosts Walmart’s supply chain management. International Journal of Information Technology and Business Management 24(1):1–9
Krishnan KS, Rao VRK (1965) Inventory control in N warehouses. J Ind Eng 16(3):212–215
Kulp SC, Lee HL, Ofek E (2004) Manufacturer benefits from information integration with retail customers. Manag Sci 50(4):431–444
Kusiak A, Zheng H, Song Z (2009) Short-term prediction of wind farm power: a data mining approach. IEEE Trans Energy Convers 24(1):125–136
Lagodimos AG, Anderson EJ (1993) Optimal positioning of safety stocks in MRP. The International Journal Of Production Research 31(8):1797–1813
Längkvist M, Karlsson L, Loutfi A (2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn Lett 42:11–24
Lazear EP (1986) Retail pricing and clearance sales. Am. Econ Rev 76(1):14–32
Lee H, Whang S (1999) Decentralized multi-echelon supply chains: incentives and information. Manag Sci 45(5):633–640
Lee H, Kim SG, Hw Park, Kang P (2014) Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach. Technol Forecast Soc Chang 86:49–64
Lee HL (1987) A multi-echelon inventory model for repairable items with emergency lateral transshipments. Manag Sci 33(10):1302–1316
Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43(4):546–558
Lee HL, So KC, Tang CS (2000) The value of information sharing in a two-level supply chain. Manag Sci 46(5):626–643
Lesnaia E (2004) Optimizing safety stock placement in general network supply chains. PhD thesis, Massachusetts Institute of Technology
Levi R, Pál M, Roundy RO, Shmoys DB (2007) Approximation algorithms for stochastic inventory control models. Math Oper Res 32(2):284–302
Liang C (2018) New Group-Buying Services Have Pinduoduo in Their Crosshairs. https://www.sixthtone.com/news/1002740/new-group-buying-services-have-pinduoduo-in-their-crosshairs (accessed December 2, 2018)
Lin G, Ettl M, Buckley S, Bagchi S, Yao DD, Naccarato BL, Allan R, Kim K, Koenig L (2000) Extended-enterprise supply-chain management at IBM personal systems group and other divisions. Interfaces 30 (1):7–25
Littlewood K (1972) Forecasting and control of passenger bookings. Airline Group International Federation of Operational Research Societies Proceedings 12:95–117
Liu Q, Van Ryzin G (2008) On the choice-based linear programming model for network revenue management. Manuf Serv Oper Manag 10(2):288–310
Ma C, Lu J, Yuan R (2018) The Secret Behind JD.com’s Super Fast Delivery. https://medium.com/jd-technology-blog/the-secret-behind-jd-coms-super-fast-delivery-71a6c0792405 (accessed February 26, 2019)
Mahajan S, van Ryzin GJ (1999) Retail inventories and consumer choice. In: Quantitative Models for Supply Chain Management, Springer, pp 491–551
Manary MP, Willems SP (2008) Setting safety-stock targets at Intel in the presence of forecast bias. Interfaces 38(2):112–122
Melacini M, Perotti S, Rasini M, Tappia E (2018) E-fulfilment and distribution in omni-channel retailing: a systematic literature review. Int J Phys Distrib Logist Manag 48(4):391–414
Melkote S, Daskin MS (2001) Capacitated facility location/network design problems. Eur J Oper Res 129 (3):481–495
Melo MT, Nickel S, Da Gama FS (2006) Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Comput Oper Res 33(1):181–208
Melo MT, Nickel S, Saldanha-Da-Gama F (2009) Facility location and supply chain management–a review. Eur J Oper Res 196(2):401–412
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv: 13013781
Mishra BK, Raghunathan S (2004) Retailer- vs. vendor-managed inventory and brand competition. Manag Sci 50(4):445–457
MWPVL International Inc. (2018) The Walmart Distribution Center Network in the United States. http://www.mwpvl.com/html/walmart.html (accessed November 19, 2018)
Nahmias S, Demmy WS (1981) Operating characteristics of an inventory system with rationing. Manag Sci 27(11):1236–1245
Nedellec R, Cugliari J, Goude Y (2014) GEFCom2012 electric load forecasting and backcasting with semi-parametric models. Int J Forecast 30(2):375–381
Nenni ME, Giustiniano L, Pirolo L (2013) Demand forecasting in the fashion industry: a review. International Journal of Engineering Business Management 5:5–36
Noham R, Tzur M (2014) The single and multi-item transshipment problem with fixed transshipment costs. Nav Res Logist (NRL) 61(8):637–664
O’Neil S, Zhao X, Sun D, Wei JC (2016) Newsvendor problems with demand shocks and unknown demand distributions. Decis Sci 47(1):125–156
Oroojlooyjadid A, Snyder L, Takáč M (2016) Applying deep learning to the newsvendor problem. arXiv: 160702177
Palmer JW, Markus ML (2000) The performance impacts of quick response and strategic alignment in specialty retailing. Inf Syst Res 11(3):241–259
Panahifar F, Heavey C, Byrne PJ, Fazlollahtabar H (2015) A framework for collaborative planning, forecasting and replenishment (CPFR) state of the art. J Enterp Inf Manag 28(6):838–871
Paterson C, Kiesmüller G, Teunter R, Glazebrook K (2011) Inventory models with lateral transshipments: a review. Eur J Oper Res 210(2):125–136
Pitkin J, Manolopoulou I, Ross G (2018) Bayesian hierarchical modelling of sparse count processes in retail analytics. arXiv: 180505657
Premkumar GP (2000) Interorganization systems and supply chain management. Inf Syst Manag 17(3):1–14
Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2017) Catboost: unbiased boosting with categorical features. arXiv: 170609516
Qi M, Yuan R, Wu D, Shen ZJM (2018) An approximation algorithm for joint replenishment and allocation problem, working paper
Ramakrishna KS, Sharafali M, Lim YF (2015) A two-item two-warehouse periodic review inventory model with transshipment. Ann Oper Res 233(1):365–381
Revelle CS, Laporte G (1996) The plant location problem: new models and research prospects. Oper Res 44(6):864–874
Rougès JF, Montreuil B (2014) Crowdsourcing delivery: New interconnected business models to reinvent delivery. In: 1st International Physical Internet Conference, pp 1–19
Ruiz-Torres AJ, Mahmoodi F (2010) Safety stock determination based on parametric lead time and demand information. Int J Prod Res 48(10):2841–2857
Rusmevichientong P, Shen ZJM, Shmoys DB (2010) Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Oper Res 58(6):1666–1680
Salameh MK (1997) Buffer stock under the effect of fluctuating demand. Prod Plan Control 8(1):37–40
Schön C (2010) On the optimal product line selection problem with price discrimination. Manag Sci 56 (5):896–902
Sethi V, Hwang KT, Pegels C (1993) Information technology and organizational performance: a critical evaluation of computerworld’s index of information systems effectiveness. Inf Manag 25(4):193–205
Shan X (2018) JD to share inventory with Wal-Mart. http://en.people.cn/n3/2017/0726/c90000-9246773.html (accessed December 2, 2018)
Shang KH, Song JS (2003) Newsvendor bounds and heuristic for optimal policies in serial supply chains. Manag Sci 49(5):618–638
Sheffi Y (2002) The value of CPFR. In: Proceedings of RIRL Conference, Lisboa
Sherbrooke CC (1968) METRIC: A multi-echelon technique for recoverable item control. Oper Res 16(1):122–141
Shi Y (2018) Solving Inventory Assortment Problem by Utilizing Product Embedding. https://medium.com/jd-technology-blog/solving-inventory-assortment-problem-by-utilizing-product-embedding-807f992cb35a (accessed December 3, 2018)
Shi Y, Qi M, Ma C, Yuan R, Wu D, Shen ZJ (2018) A practical end-to-end deep learning inventory model for e-commerce. Working Paper
Shocker AD, Bayus BL, Kim N (2004) Product complements and substitutes in the real world: the relevance of “other products”. J Mark 68(1):28–40
Shukla M, Jharkharia S (2013) Agri-fresh produce supply chain management: a state-of-the-art literature review. Int J Oper Prod Manag 33(2):114–158
Snyder LV (2006) Facility location under uncertainty: a review. IIE Trans 38(7):547–564
Snyder LV, Shen ZJM (2011) Fundamentals of Supply Chain Theory. Wiley, Hoboken
Sridharan R (1995) The capacitated plant location problem. Eur J Oper Res 87(2):203–213
Srinivasan K, Kekre S, Mukhopadhyay T (1994) Impact of electronic data interchange technology on JIT shipments. Manag Sci 40(10):1291–1304
Stein T, Sweat J (1998) Killer supply chains. Information Week 708(9):36–46
Sui Z, Gosavi A, Lin L (2010) A reinforcement learning approach for inventory replenishment in vendor-managed inventory systems with consignment inventory. Eng Manag J 22(4):44–53
Suykens JAK, Vandewalle J (2000) Recurrent least squares support vector machines. IEEE Transactions on Circuits and Systems Part 1 Fundamental Theory and Applications 47(7):1109–1114
Tagaras G (1989) Effects of pooling on the optimization and service levels of two-location inventory systems. IIE Trans 21(3):250–257
Taieb SB, Atiya AF (2016) A bias and variance analysis for multistep-ahead time series forecasting. IEEE Transactions on Neural Networks and Learning Systems 27(1):62–76
Talluri K, Van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Manag Sci 50(1):15–33
Topkis DM (1968) Optimal ordering and rationing policies in a nonstationary dynamic inventory model with n demand classes. Manag Sci 15(3):160–176
Torabi SA, Hassini E, Jeihoonian M (2015) Fulfillment source allocation, inventory transshipment, and customer order transfer in e-tailing. Transportation Research Part E:, Logistics and Transportation Review 79:128–144
Tyralis H, Papacharalampous G (2017) Variable selection in time series forecasting using random forests. Algorithms 10(4):114
Veinott AF Jr (1965) Optimal policy for a multi-product, dynamic, nonstationary inventory problem. Manag Sci 12(3):206–222
Verhoef PC, Kannan PK, Inman JJ (2015) From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. J Retail 91(2):174–181
Verity JW (1996) Clearing the cobwebs from the stockroom. Bus Week 3498:140
Vulcano G, Van Ryzin G, Chaar W (2010) Choice-based revenue management: an empirical study of estimation and optimization. Manuf Serv Oper Manag 12(3):371–392
Wang R (2012) Capacitated assortment and price optimization under the multinomial logit model. Oper Res Lett 40(6):492– 497
Warszawski A, Peer S (1973) Optimizing the location of facilities on a building site. J Oper Res Soc 24 (1):35–44
Wen R, Torkkola K, Narayanaswamy B (2017) A multi-horizon quantile recurrent forecaster. arXiv: 171111053
Williams CK, Rasmussen CE (1996) Gaussian processes for regression. In: Advances in Neural Information Processing Systems, pp 514–520
Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci 6(3):324–342
Wong H, van Houtum GJ, Cattrysse D, Van Oudheusden D (2006) Multi-item spare parts systems with lateral transshipments and waiting time constraints. Eur J Oper Res 171(3):1071–1093
Wu D (2018) Going the Extra Mile In Customer Service: How Does JD.com Make Order Cancellation So Easy? https://medium.com/jd-technology-blog/going-the-extra-mile-in-customer-service-how-does-jd-com-make-order-cancellation-so-easy-a00ebc7fd1ee (accessed February 26, 2019)
Xu PJ, Allgor R, Graves SC (2009) Benefits of reevaluating real-time order fulfillment decisions. Manuf Serv Oper Manag 11(2):340–355
Xu Q, Liu X, Jiang C, Yu K (2016) Quantile autoregression neural network model with applications to evaluating value at risk. Appl Soft Comput 49:1–12
Yang J, Qin Z (2007) Capacitated production control with virtual lateral transshipments. Oper Res 55 (6):1104–1119
Yao Y, Dresner M (2008) The inventory value of information sharing, continuous replenishment, and vendor-managed inventory. Transportation Research Part E:, Logistics and Transportation Review 44(3):361–378
Young E (2016) Amazon UK transshipment stow process analysis through discrete event simulation. Master’s thesis, Massachusetts Institute of Technology
Yuan R, Jing L (2018) Highlights from the Global Optimization Challenge. https://medium.com/jd-technology-blog/highlights-from-the-global-optimization-challenge-17f4ad357337 (accessed December 12, 2018)
Zeppieri MA (2004) Predictive capacity planning modeling with tactical and strategic applications. Master’s thesis, Massachusetts Institute of Technology
Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175
Zhang H, Chao X, Shi C (2017) Closing the gap: A learning algorithm for the lost-sales inventory system with lead times. Working Paper
Zhang Y, Gao J (2017) Assessing the performance of deep learning algorithms for newsvendor problem. In: International Conference on Neural Information Processing, Springer, pp 912–921
Zhang Y, Huang M, Hu X, Sun L (2018) Package consolidation approach to the split-order fulfillment problem of online supermarkets. J Oper Res Soc 69(1):127–141
Zheng S (2010) Boosting based conditional quantile estimation for regression and binary classification. In: Mexican International Conference on Artificial Intelligence, Springer, pp 67–79
Zheng Z, Su D (2014) Short-term traffic volume forecasting: a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm. Transportation Research Part C:, Emerging Technologies 43:143–157
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Ge, D., Pan, Y., Shen, ZJ.(. et al. Retail supply chain management: a review of theories and practices. J. of Data, Inf. and Manag. 1 , 45–64 (2019). https://doi.org/10.1007/s42488-019-00004-z
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The Effect of E-commerce Expansion on Local Retail
E -commerce has dramatically altered retail in the last two decades, with online sales growing from 0.63 percent of total retail sales in 1999 to 13.3 percent in 2021. In Creative Destruction? Impact of E-commerce on the Retail Sector (NBER Working Paper 30077), Sudheer Chava , Alexander Oettl , Manpreet Singh , and Linghang Zeng study how the rise of online selling has affected brick-and-mortar retail establishments and their employees.
The researchers measure the presence of e-commerce in an area using the staggered rollout of a major e-commerce retailer’s fulfillment centers across the United States. This firm had only three such centers in 2000, but more than 90 by the end of 2016. Establishing a center reduces shipment delivery times in surrounding areas, which may encourage local consumers to shift towards e-commerce and away from traditional brick-and-mortar retailers. The researchers study the trends in retail outcomes before and after a fulfillment center is established in proximate counties, and compare them with the trends in a control group of counties near where centers would be built at a later date.
Establishment of an e-commerce fulfillment center reduces retail employment growth in the host county by an average of almost 1,000 jobs per quarter.
The researchers first use a detailed employer-employee payroll dataset covering 2.6 million retail workers to explore how e-commerce growth affects traditional retail workers. The establishment of a fulfillment center is associated with a drop of 2.5 percent on average in the income of hourly retail workers in the same county, or about $825 annually. The income drop is similar for retail workers in counties within 100 miles of the center, but is negligible beyond that distance. Hourly workers’ income losses arise primarily from a reduction in hours worked.
A new fulfillment center is not associated with lower income among salaried retail workers, and the impacts of e-commerce on hourly workers’ income vary substantially with worker characteristics. Income falls the most among the youngest and oldest workers, with income falling by 3.6 percent among workers over age 64. Workers with longer tenure at retail firms see smaller losses in income, and income losses are larger among part-time workers. Income losses are largest among workers in general-merchandise and home-improvement stores, two retail subsectors where customers may place less value on their in-store experience.
The researchers next use National Establishment Time Series data to examine the impact of e-commerce fulfillment centers on traditional retail stores. Sales in the average brick-and-mortar retail store fall by about 4 percent after a local fulfillment center opens. Stores react to these drops by reducing hours per worker and by cutting employee headcount by 2.1 percent on average, or about 36 workers per 100 stores. The opening of a fulfillment center coincides with a 3 percentage point increase in the annual likelihood that brick-and-mortar stores close. The largest impacts are found among smaller and newer stores. Likewise, new stores’ rate of entry falls by 8.1 percentage points within 100 miles of a new center.
Finally, the researchers use data from the Quarterly Census of Employment and Wages to examine how e-commerce expansion affects retail employment and wage growth at the county level. Establishment of a fulfillment center is associated with reduced employment growth in retail of 2.9 percent within the same county, a loss of about 938 jobs per county per quarter, with smaller losses within a 100-mile radius. These retail losses are partially offset by increased local employment in transportation and warehousing, which gain about 256 jobs, and in restaurants, which gain about 143 jobs. Opening a new fulfillment center also coincides with increased wage growth in warehousing.
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The Role and Impact of Artificial Intelligence on Retail Business and its Developments
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