A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing
Abstract
:1. Introduction
1.1. Retailing
1.2. Artificial Intelligence (AI) and Machine Learning (ML)
2. Research Methodology
3. Application of ML Techniques for Different Problem Types in Retailing
3.1. Classification
3.2. Prediction
3.3. Clustering
3.4. Optimization
3.5. Anomaly Detection
3.6. Ranking
3.7. Recommendation
3.8. Diffusion of Machine Learning within the Largest Retail Cooperations
4. Machine Learning Application Scenarios in the Value-Adding Core Processes
4.1. Managing Goods
4.2. Ordering Goods
4.3. Serving Customers
4.4. Transporting Goods
4.5. Handing out Goods
4.6. Making Goods Available
4.7. Financial Accounting
5. Discussion
Author Contributions
Conflicts of Interest
References
- Schütte, R. Information Systems for Retail Companies. In Proceedings of the 29th International Conference on Advanced Information Systems Engineering, CAiSE 2017, Essen, Germany, 12–16 June 2017; Dubois, E., Pohl, K., Eds.; Springer: Berlin, Germany, 2017; pp. 13–25. [Google Scholar]
- Becker, J.; Schütte, R. Handelsinformationssysteme Domänenorientierte Einführung in die Wirtschaftsinformatik, 2nd ed.; Redline-Wirtschaft: Frankfurt/M., Germany, 2004. [Google Scholar]
- Borden, N.H. The concept of the marketing mix. J. Advert. Res. 1964, 4, 2–7. [Google Scholar]
- Haller, S. Handelsmarketing; 3. Auflage ed. Modernes Marketing für Studium und Praxis, ed.; Weis, H.-C., Ed.; Fridrich Kiehl Verlag: Ludwigshafen, Germany, 2008. [Google Scholar]
- McCarthy, E. Basic Marketing: A Managerial Approach; Irwin: Georgtown, IN, USA, 1960. [Google Scholar]
- McCarthy, J.; Minsky, M.L.; Rochester, N.; Shannon, C.E. A proposal for the dartmouth summer research project on artificial intelligence. AI Mag. 1955, 27, 12. [Google Scholar]
- Computing Machinery and Intelligence. Available online: https://books.google.com.hk/books?hl=en&lr=&id=rQGiUlAtpQUC&oi=fnd&pg=PA213&dq=Computing+machinery+and+intelligence&ots=VXvI7lAhTg&sig=aacArMhq_3blRum6IV3NX6NEEnE&redir_esc=y#v=onepage&q=Computing%20machinery%20and%20intelligence&f=false (accessed on 22 January 2019).
- Russell, S.J.; Norvig, P. Artificial Intelligence: A Modern Approach; Pearson Education Limited: Kuala Lumpur, Malaysia, 2016. [Google Scholar]
- Goldberg, D.E.; Holland, J.H. Genetic algorithms and machine learning. Mach. Learn. 1988, 3, 95–99. [Google Scholar] [CrossRef]
- Meffert, H.; Burmann, C.; Kirchgeorg, M. Marketing: Grundlagen Marktorientierter Unternehmensführung Konzepte—Instrumente—Praxisbeispiele, 12th ed.; Springer Fachmedien Wiesbaden: Wiesbaden, Germany, 2015; pp. 357–768. [Google Scholar]
- Daurer, S.; Molitor, D.; Spann, M. Digitalisierung und Konvergenz von Online-und Offline-Welt. Zeitschrift für Betriebswirtschaft 2012, 82, 3–23. [Google Scholar] [CrossRef]
- Glaeser, H.R. Qualitätsbausteine für die Beschäftigung im Einzelhandel. In Landschaftsverband Westfalen-Lippe Messe der Integrationsunternehmen; LWL: Münster, Germany, 2014. [Google Scholar]
- Lorentschitsch, B. Der Lebensmittelhandel im Spannungsfeld zwischen gesellschaftlicher Verantwortung und Geiz ist geil. In CSR und Lebensmittelwirtschaft; Willers, C., Ed.; Springer: Berlin, Germany, 2016; pp. 331–344. [Google Scholar]
- EHI Retail Institute. Nettoumsatz der Weltweit Größten Einzelhändler. 2016. Available online: https://www.handelsdaten.de/nettoumsatz-der-weltweit-groessten-einzelhaendler-2016 (accessed on 11 November 2017).
- Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M. Machine Learning: An Artificial Intelligence Approach; Springer Science & Business Media: Berlin, Germany, 2013. [Google Scholar]
- Jordan, M.I.; Mitchell, T.M. Machine learning: Trends, perspectives, and prospects. Science 2015, 349, 255–260. [Google Scholar] [CrossRef] [PubMed]
- Chui, M.; Francisco, S. Artificial Intelligence the Next Digital Frontier? McKinsey and Company Global Institute: New York, NY, USA, 2017; Volume 47. [Google Scholar]
- Retail Week Live: How Are Retailers Leading the Way with AI? Available online: https://www.retail-week.com/retail-week-live/retail-week-live-early-adopters-of-ai-will-benefit/7028186.article?authent=1 (accessed on 2 Jaunary 2018).
- Home Depot to Add 1,000 Tech Jobs. Available online: https://www.retaildive.com/news/home-depot-to-add-1000-tech-jobs/521673/ (accessed on 22 July 2018).
- CVS Health and Aetna Bet $69 Billion Merger on Analytics, Data, Digital Transformation. Available online: https://www.zdnet.com/article/cvs-health-and-aetna-bet-69-billion-merger-on-analytics-data-digital-transformation/ (accessed on 8 July 2018).
- Using Advanced Analytics to Smooth Member Transitions. Available online: https://payorsolutions.cvshealth.com/insights/using-advanced-analytics-to-smooth-member-transitions (accessed on 11 May 2018).
- Kroger Using Data, Technology to ‘Restock’ for the Future. Available online: https://consumergoods.com/kroger-using-data-technology-restock-future (accessed on 8 Jaunary 2018).
- 84.51° Builds a Machine Learning Machine for Kroger. Available online: https://www.forbes.com/sites/tomdavenport/2018/04/02/84-51-builds-a-machine-learning-machine-for-kroger/ (accessed on 11 July 2018).
- Eden: The Tech That’s Bringing Fresher Groceries to You. Available online: https://blog.walmart.com/innovation/20180301/eden-the-tech-thats-bringing-fresher-groceries-to-you (accessed on 12 March 2018).
- Kroger Finally Realizes It Needs Tech to Survive. Available online: https://techhq.com/2018/05/kroger-finally-realizes-it-needs-tech-to-survive/ (accessed on 20 August 2018).
- See a Doctor Virtually with MDLIVE. Available online: https://www.walgreens.com/topic/pharmacy/virtualdoctor.jsp (accessed on 22 August 2018).
- Amazon Scams On The Rise As Fraudulent Sellers Run Amok And Profit Big. Available online: https://www.forbes.com/sites/wadeshepard/2017/01/02/amazon-scams-on-the-rise-in-2017-as-fraudulent-sellers-run-amok-and-profit-big/#5261789f3ea6 (accessed on 1 November 2018).
- Sorokina, D.; Cantú-Paz, E. Amazon Search: The Joy of Ranking Products. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Tuscany, Italy, 17–21 July 2016. [Google Scholar]
- Lidl Launches Online Chatbot That Recommends Wine Based on Your Budget and Food Choices. Available online: https://www.telegraph.co.uk/business/2018/01/31/lidl-launches-online-chatbot-recommends-wine-based-budget-food/ (accessed on 1 Feburary 2018).
- Linden, G.; Smith, B.; York, J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 2003, 7, 76–80. [Google Scholar]
- Carrefour and Sirqul Launch Smart Retail Store in Taiwan. Available online: https://corp.sirqul.com/carrefour-sirqul-launch-smart-retail-store-taiwan/ (accessed on 11 June 2018).
- Kephart, J.O.; Hanson, J.E.; Greenwald, A.R. Dynamic pricing by software agents. Comput. Netw. 2000, 32, 731–752. [Google Scholar] [CrossRef]
- Jaekel, M. Die Macht der Digitalen Plattformen: Wegweiser im Zeitalter Einer Expandierenden Digitalsphäre und künstlicher Intelligenz; Springer: Wiesbaden, Germany, 2017. [Google Scholar]
- Kuo, R.J.; Wu, P.; Wang, C. An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination. Neural Netw. 2002, 15, 909–925. [Google Scholar] [CrossRef]
- New Virtual Store Remodeling Solution Enables Customer-Driven Store Design and Testing in a Virtual Reality Environment. Available online: https://www.symphonyretailai.com/new-virtual-store-remodeling-enables-customer-driven-store-design-testing/ (accessed on 11 August 2018).
- Leo Kumar, S.P. State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing. Eng. Appl. Artif. Intell. 2017, 65, 294–329. [Google Scholar] [CrossRef]
- Montgomery, A.L.; Smith, M.D. Prospects for Personalization on the Internet. J. Interactive Mark. 2009, 23, 130–137. [Google Scholar] [CrossRef]
- Blue Yonder’s Technology Delivers Improved Product Availability for Morrisons With Shelf Gaps Down 30%. Available online: https://www.blueyonder.ai/sites/default/files/by-en-case-study-morrisons_0.pdf (accessed on 22 July 2018).
- Šustrová, T. A Suitable Artificial Intelligence Model for Inventory Level Optimization. Trends Econ. Manag. Trendy Ekon. Manag. 2016, 10, 48–55. [Google Scholar] [CrossRef]
- Landa-Silva, D.; Marikar, F.; Le, K. Heuristic approach for automated shelf space allocation. In Proceedings of the 2009 ACM Symposium on Applied Computing, Honolulu, HI, USA, 9–12 March 2009. [Google Scholar]
- Huh, W.T.; Janakiraman, G.; Muckstadt, J.A.; Rusmevichientong, P. An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand. Math. Oper. Res. 2009, 34, 397–416. [Google Scholar] [CrossRef]
- Get Under Customers’ Skin with AI Personalisation. Available online: https://www.thegrocer.co.uk/channels/online/get-under-customers-skin-with-ai/554223.article (accessed on 20 August 2018).
- Weinbren, E. M&S to Use Artificial Intelligence to Reduce Bakery Waste. 2017. Available online: https://m.thegrocer.co.uk/home/topics/waste-not-want-not/ms-to-use-artificial-intelligence-to-reduce-bakery-waste/560456.article (accessed on 20 August 2018).
- Ning, A.; Lau, H.C.; Zhao, Y.; Wong, T.T. Fulfillment of Retailer Demand by Using the MDL-Optimal Neural Network Prediction and Decision Policy. IEEE Trans. Ind. Inform. 2009, 5, 495–506. [Google Scholar] [CrossRef]
- Kawa, A.; Pieranski, B.; Zdrenka, W. Dynamic Configuration of Same-Day Delivery in E-commerce. In Modern Approaches for Intelligent Information and Database Systems; Sieminski, A., Ed.; Springer: Berlin, Germany, 2018; pp. 305–315. [Google Scholar]
- Dennis, L. Future of Retail Round-Up. 2018. Available online: http://www.retail-focus.co.uk/features/1339-future-of-retail-round-up (accessed on 20 August 2018).
- Bertasius, G.; Park, H.S.; Yu, S.X.; Shi, J. First person action-object detection with egonet. arXiv, 2016; arXiv:1603.04908. [Google Scholar]
- Rüschen, S.; Wiehenbrauk, D. Disruption in Retail–Retail 4.0. In Mobile Payment; Springer: Berlin, Germany, 2017; pp. 49–65. [Google Scholar]
- Frangoul, A. How Robots Are Helping to Shape the Future of Retail. 2017. Available online: https://www.cnbc.com/2017/11/22/how-robots-are-helping-to-shape-the-future-of-retail.html (accessed on 11 May 2018).
- Holmqvist, J.; van Vaerenbergh, Y.; Grönroos, C. Language use in services: Recent advances and directions for future research. J. Bus. Res. 2017, 72, 114–118. [Google Scholar] [CrossRef]
- Pierdicca, R.; Liciotti, D.; Contigiani, M.; Frontoni, E.; Mancini, A.; Zingaretti, P. Low cost embedded system for increasing retail environment intelligence. In Proceedings of the 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Turin, Italy, 29 June–3 July 2015. [Google Scholar]
- Tarantilis, C.D.; Kiranoudis, C.T. A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector. Eur. J. Oper. Res. 2007, 179, 806–822. [Google Scholar] [CrossRef]
- Wurman, P.R.; D’Andrea, R.; Mountz, M. Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag. 2008, 29, 9. [Google Scholar]
- Stevens, L.; Phillips, E. Amazon Puzzles Over the Perfect Fit—in Boxes. 2017. Available online: https://www.wsj.com/articles/amazon-aims-for-one-box-fits-all-1513765800 (accessed on 20 Jaunary 2018).
- Xu, X.-F.; Chang, W.-H.; Liu, J. Resource allocation optimization model of collaborative logistics network based on bilevel programming. Sci. Programm. 2017, 2017, 4587098. [Google Scholar] [CrossRef]
- Armstrong, L. The Weather Company, an IBM Business, Introduces New Solutions Designed to Help Freight and Logistic Companies Improve Operational Efficiency. 2017. Available online: https://www-03.ibm.com/press/us/en/pressrelease/52381.wss (accessed on 28 August 2018).
- Leung, K.H.; Choy, K.L.; Siu, P.K.; Ho, G.T.; Lam, H.Y.; Lee, C.K. A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process. Expert Syst. Appl. 2018, 91, 386–401. [Google Scholar] [CrossRef]
- Lam, H.Y.; Choy, K.L.; Ho, G.T.S.; Cheng, S.W.; Lee, C.K.M. A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment. Int. J. Prod. Econ. 2015, 170, 763–779. [Google Scholar] [CrossRef]
- Vanian, J. Why Walmart Is Testing Robots In Stores—And Here’s What It Learned. 2018. Available online: http://fortune.com/2018/03/26/walmart-robot-bossa-nova/ (accessed on 1 May 2018).
- Kumar, D.T.; Soleimani, H.; Kannan, G. Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS. Int. J. Appl. Math. Comput. Sci. 2014, 24, 669–682. [Google Scholar] [CrossRef]
- Suponenkovs, A.; Sisojevs, A.; Mosāns, G.; Kampars, J.; Pinka, K.; Grabis, J.; Taranovs, R. Application of image recognition and machine learning technologies for payment data processing review and challenges. In Proceedings of the 2017 5th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), Riga, Latvia, 24–25 November 2017. [Google Scholar]
- Arianto, A.D.; Affandi, A.; Nugroho, S.M.S. Opinion detection of public sector financial statements using K-nearest neighbors. In Proceedings of the 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, Indonesia, 19–21 September 2017. [Google Scholar]
- McAfee, A.; Brynjolfsson, E. Big data: The management revolution. Harvard Bus. Rev. 2012, 90, 60–68. [Google Scholar]
- Litzinger, D. Computergestütztes Promotioncontrolling: Konzeption eines Informationssystems für das Controlling von Konsumgüterpromotions; Springer: Berlin, Germany, 2013. [Google Scholar]
- Schütte, R.; Vetter, T. Analyse des Digitalisierungspotentials von Handelsunternehmen. In Handel 4.0; Springer: Berlin, Germany, 2017; pp. 75–113. [Google Scholar]
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Weber, F.; Schütte, R. A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing. Big Data Cogn. Comput. 2019, 3, 11. https://doi.org/10.3390/bdcc3010011
Weber F, Schütte R. A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing. Big Data and Cognitive Computing. 2019; 3(1):11. https://doi.org/10.3390/bdcc3010011
Chicago/Turabian StyleWeber, Felix, and Reinhard Schütte. 2019. "A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing" Big Data and Cognitive Computing 3, no. 1: 11. https://doi.org/10.3390/bdcc3010011