{"id":31849,"date":"2018-11-13T19:12:37","date_gmt":"2018-11-14T00:12:37","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/hm-bets-on-machine-learning-as-last-ditch-effort-to-compete\/"},"modified":"2018-11-13T19:12:37","modified_gmt":"2018-11-14T00:12:37","slug":"hm-bets-big-on-machine-learning-to-survive","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/hm-bets-big-on-machine-learning-to-survive\/","title":{"rendered":"H&M Bets Big on Machine-Learning to Survive"},"content":{"rendered":"

Fast-Fashion Industry Dynamics<\/strong><\/p>\n

The rise of fast-fashion brands such as Zara, H&M, Top Shop, and Forever 21 has contributed to the decline of the traditional bi-annual fashion seasons and the emergence of near weekly \u201cmicro-seasons\u201d.[1]<\/sup><\/a> The success of the fast-fashion business model hinges on anticipating micro fashion trends and bringing them to market quickly and at low cost.[2]<\/sup><\/a> Since fast-fashion retailers are focused on predicting rather than creating fashion trends, it is critical that their predictions are correct; otherwise, they risk getting stuck with inventory that they can\u2019t move once the next \u201cmicro-trend\u201d begins. As a result, fast-fashion retailers are turning to machine-learning to help detect trends and avoid an unpopular and costly product cycles.<\/p>\n

H&M Stock Hits 10-year Low as it Struggles to Keep Up with Competitors<\/strong><\/p>\n

H&M has struggled to keep up with other fast-fashion retailers in predicting retail trends and localizing their merchandise to appeal to consumer tastes. In September of this year, H&M\u2019s stock price hit a more than 10-year low (see Chart 1<\/strong>) after reporting that pre-tax profits shrank nearly 20% from the previous year.[3]<\/sup><\/a><\/p>\n

\"\"<\/a>
Chart 1: H&M Stock Price (in SEK) on Stockholm Stock Exchange (Jan \u201800 – Nov \u201818)<\/figcaption><\/figure>\n

H&M\u2019s declining performance can be attributed to two key factors. First, H&M has consistently failed to predict and respond to fashion trends ahead of competitors. In March 2017, Goldman Sachs reported that H&M\u2019s supply chain lead times are double those of Zara.[4]<\/sup><\/a> As a result, H&M\u2019s inability to execute quickly has left the company with nearly $4B of unsold inventory.[5]<\/sup><\/a> Second, H&M failed to understand consumer preferences in key markets. According to Forbes, \u201cyou could walk into any H&M store whether it was located in Sweden, the United Kingdom or the United States and it would carry very similar merchandise\u201d.[6]<\/sup><\/a><\/p>\n

H&M Looks to Machine-Learning for Turnaround Efforts<\/strong><\/p>\n

In an effort to improve performance, H&M is turning to machine-learning. The Wall Street Journal<\/em> reports that H&M plans to analyze store receipts, returns, and loyalty card data to better align supply and demand and reduce reliance on markdowns.[7]<\/sup><\/a> H&M piloted this approach in their \u00d6stermalm, Stockholm store.[8]<\/sup><\/a> The store had previously been stocked with basics for men, women, and children\u2014but after using machine-learning to analyze purchase history, they learned that most of the store\u2019s customers were women.[9]<\/sup><\/a> As a result, the store was able to reduce the number of items it stocked by 40%, adding more fashion-forward items for women and completely<\/em> removing its menswear line.[10]<\/sup><\/a><\/p>\n

Emboldened by the early success of the Stockholm\u00a0 pilot, H&M is now investing heavily in machine-learning to inform assortment and demand planning. Rather than relying on merchants to predict trends, H&M has built a team of 200 data scientists, analysts, and engineers to analyze data ranging from external blog posts to internal purchasing data.[11]<\/sup><\/a> In addition to using machine-learning algorithms to build better assortments, H&M is investing in automated warehouses, with the ultimate goal of achieving next-day delivery for 90% of the European market.[12]<\/sup><\/a> Long term, H&M is hoping to implement RFID technology in its stores to further improve efficiencies in its supply chain.[13]<\/sup><\/a> The RFID technology would allow customers to scan labels and receive personalized recommendations based on their purchase history or interests.[14]<\/sup><\/a><\/p>\n

Thinking Beyond Machine-Learning<\/strong><\/p>\n

H&M is making a big bet on machine-learning to turn the company around from a failing chain retailer to a digitally integrated brand. Unfortunately, this effort may be several years too late. The positive results from the Stockholm store pilot are encouraging, but given H&M\u2019s massive 4,288 store portfolio, I recommend further validating its investment by piloting the technology in a critical mass of stores that is indicative of H&M\u2019s global store portfolio prior to rolling this initiative out to all stores. Also, rather than solely focusing on the rapid implementation of technology, I recommend that H&M invest in radically re-building its culture and bringing in fresh talent that aligns with its new company vision. In an interview with Women\u2019s Wear Daily<\/em>, H&M\u2019s CEO, Karl-Johan Persson, rejected the need to change company culture explaining that \u201cthe recent reasons why we did some mistakes connected to the H&M brand and physical stores is because we haven\u2019t been customer focused enough, we haven\u2019t lived [our] values well enough, so it\u2019s more revisiting that.\u201d[15]<\/sup><\/a> I think that customer-focus is exactly<\/em> the cultural mindset that H&M lacks. Unfortunately for H&M, by the time its senior management team realizes this, no pivot will be able to turn the company around. If machine-learning is in fact the answer to H&M\u2019s problems, given its 3-year slump and all-time low stock price, does the company have the luxury of time to see through the benefits that the technology can offer?<\/p>\n

Word Count: 791<\/p>\n

 <\/p>\n

Footnotes<\/strong><\/p>\n

[1]<\/sup><\/a> \u201cThe Future of Fashion: From Design to Merchandising, How Tech Is Reshaping the Industry.\u201d CB Insights Research<\/em>, 28 Feb. 2018, www.cbinsights.com\/research\/fashion-tech-future-trends\/.<\/p>\n

[2]<\/sup><\/a> Marr, Bernard. \u201cHow Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.\u201d Forbes Magazine<\/em>, 10 Aug. 2018, https:\/\/bit.ly\/2QAczkQ.<\/p>\n

[3]<\/sup><\/a> \u201cH&M’s Q3 Pretax Profit Falls More than Expected.\u201d Thomson Reuters<\/em>, 27 Sept. 2018, reut.rs\/2B412oo.<\/p>\n

[4]<\/sup><\/a> Ringstrom, Anna. \u201cH&M Invests in Supply Chain as Fashion Rivalry Intensifies.\u201d Thomson Reuters<\/em>, 30 Mar. 2017, https:\/\/in.reuters.com\/article\/h-m-results-idINKBN1711G5.<\/p>\n

[5]<\/sup><\/a> Chaudhuri, Saabira. \u201cH&M Pivots to Big Data to Spot Next Fast-Fashion Trends.\u201d Wall Street Journal<\/em>, 07 May 2018, https:\/\/on.wsj.com\/2rr9qs2.<\/p>\n

[6]<\/sup><\/a> Marr, Bernard. \u201cHow Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.\u201d<\/p>\n

[7]<\/sup><\/a> Chaudhuri, Saabira. \u201cH&M Pivots to Big Data to Spot Next Fast-Fashion Trends.\u201d<\/p>\n

[8]<\/sup><\/a> Ibid.<\/p>\n

[9]<\/sup><\/a> Ibid.<\/p>\n

[10]<\/sup><\/a> Ibid.<\/p>\n

[11]<\/sup><\/a> Ibid.<\/p>\n

[12]<\/sup><\/a> Marr, Bernard. \u201cHow Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.\u201d<\/p>\n

[13]<\/sup><\/a> Ibid.<\/p>\n

[14]<\/sup><\/a> Ibid.<\/p>\n

[15]<\/sup><\/a> Diderich, Joelle. \u201cKarl-Johan Persson on Strategy and Culture.\u201d Women\u2019s Wear Daily<\/em>. 15 February 2018. https:\/\/bit.ly\/2RMYMr2<\/p>\n","protected":false},"excerpt":{"rendered":"

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