H&M Bets Big on Machine-Learning to Survive - Technology and Operations Management (2024)

Fast-Fashion Industry Dynamics

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 “micro-seasons”.[1] 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] 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’t move once the next “micro-trend” begins. As a result, fast-fashion retailers are turning to machine-learning to help detect trends and avoid an unpopular and costly product cycles.

H&M Stock Hits 10-year Low as it Struggles to Keep Up with Competitors

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’s stock price hit a more than 10-year low (see Chart 1) after reporting that pre-tax profits shrank nearly 20% from the previous year.[3]

H&M’s 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’s supply chain lead times are double those of Zara.[4] As a result, H&M’s inability to execute quickly has left the company with nearly $4B of unsold inventory.[5] Second, H&M failed to understand consumer preferences in key markets. According to Forbes, “you 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”.[6]

H&M Looks to Machine-Learning for Turnaround Efforts

In an effort to improve performance, H&M is turning to machine-learning. The Wall Street Journal 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] H&M piloted this approach in their Östermalm, Stockholm store.[8] The store had previously been stocked with basics for men, women, and children—but after using machine-learning to analyze purchase history, they learned that most of the store’s customers were women.[9] 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 removing its menswear line.[10]

Emboldened by the early success of the Stockholm 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] 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] Long term, H&M is hoping to implement RFID technology in its stores to further improve efficiencies in its supply chain.[13] The RFID technology would allow customers to scan labels and receive personalized recommendations based on their purchase history or interests.[14]

Thinking Beyond Machine-Learning

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’s 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’s 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’s Wear Daily, H&M’s CEO, Karl-Johan Persson, rejected the need to change company culture explaining that “the recent reasons why we did some mistakes connected to the H&M brand and physical stores is because we haven’t been customer focused enough, we haven’t lived [our] values well enough, so it’s more revisiting that.”[15] I think that customer-focus is exactly 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’s 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?

Word Count: 791

Footnotes

[1] “The Future of Fashion: From Design to Merchandising, How Tech Is Reshaping the Industry.” CB Insights Research, 28 Feb. 2018, www.cbinsights.com/research/fashion-tech-future-trends/.

[2] Marr, Bernard. “How Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.” Forbes Magazine, 10 Aug. 2018, https://bit.ly/2QAczkQ.

[3] “H&M’s Q3 Pretax Profit Falls More than Expected.” Thomson Reuters, 27 Sept. 2018, reut.rs/2B412oo.

[4] Ringstrom, Anna. “H&M Invests in Supply Chain as Fashion Rivalry Intensifies.” Thomson Reuters, 30 Mar. 2017, https://in.reuters.com/article/h-m-results-idINKBN1711G5.

[5] Chaudhuri, Saabira. “H&M Pivots to Big Data to Spot Next Fast-Fashion Trends.” Wall Street Journal, 07 May 2018, https://on.wsj.com/2rr9qs2.

[6] Marr, Bernard. “How Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.”

[7] Chaudhuri, Saabira. “H&M Pivots to Big Data to Spot Next Fast-Fashion Trends.”

[8] Ibid.

[9] Ibid.

[10] Ibid.

[11] Ibid.

[12] Marr, Bernard. “How Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.”

[13] Ibid.

[14] Ibid.

[15] Diderich, Joelle. “Karl-Johan Persson on Strategy and Culture.” Women’s Wear Daily. 15 February 2018. https://bit.ly/2RMYMr2

H&M Bets Big on Machine-Learning to Survive - Technology and Operations Management (2024)

FAQs

H&M Bets Big on Machine-Learning to Survive - Technology and Operations Management? ›

H&M Looks to Machine-Learning for Turnaround Efforts

How does H&M use artificial intelligence to predict trends? ›

H&M: H&M uses AI-driven tools for data visualization to forecast trends and support product development choices. It stays ahead of consumer preferences with its trend visualization tool thus holding its place as the world's leading fast-fashion brand.

How is H&M using big data? ›

The company uses big data to analyse customer needs at a local level. H&M Group is leveraging AI to achieve a climate positive value chain by 2040. The clothing retailer uses AI-driven demand prediction to optimise the supply chain, said Linda Leopold, head of AI at H&M.

What worries you most about machine learning and AI describe a problem raised by machine learning in our society? ›

Noisy data, incomplete data, inaccurate data, and unclean data lead to less accuracy in classification and low-quality results. Hence, data quality can also be considered as a major common problem while processing machine learning algorithms.

How does H&M use technology? ›

During 2020, H&M Group launched the AI tool Movebox, an algorithm that enables redistribution of products to locations where there is demand. Looking ahead, using this algorithm will also make it possible to react faster to changes in customer preferences, which will lead to a decrease in over production.

How H&M group is using technology to shape fashion's future? ›

The H&M Innovation Metaverse Design Story collection explores three scenarios in the future of fashion: ready-to-wear (including womenswear, menswear and accessories), digital and rental. Virtual try-on using 3D filters in the H&M app, or on Snapchat, make the metaverse inspired garments available to wear digitally.

What is the key competitive advantage of H&M? ›

Key aspects of H&M's business model include buying products in large volumes directly from over 700 suppliers, having efficient distribution, setting competitive prices with few middlemen, and locating stores in prime locations worldwide.

What strategy does H&M use? ›

H&M's marketing strategy revolves around its commitment to offering fashion-forward designs at affordable prices. The brand's fast-fashion model allows it to quickly respond to emerging trends and introduce new collections frequently.

What is the main purpose of H&M? ›

Every successful enterprise is guided by a strategic compass, and for H&M, this is encapsulated in its mission statement. The statement reads, "Our mission is to offer fashion and quality at the best price." This single-line mantra serves as an operational blueprint that communicates H&M's goals and commitments.

What are the five main challenges of machine learning? ›

5 Key Challenges in Machine Learning Development Process
  • 1: Achieving Performant Weights in Machine Learning Algorithms.
  • 2: Choosing the Right Loss Function.
  • 3: Controlling Learning Rate Schedules.
  • 4: Coping with Innate Randomness in a Machine Learning Model.
  • 5: Achieving 'Useful Dissonance' in a Training Data Set.

What problems can be solved with machine learning? ›

Real-World Examples of Machine Learning (ML)
  • Facial recognition. ...
  • Product recommendations. ...
  • Email automation and spam filtering. ...
  • Financial accuracy. ...
  • Social media optimization. ...
  • Healthcare advancement. ...
  • Mobile voice to text and predictive text. ...
  • Predictive analytics.

What is the impact of AI and machine learning? ›

AI has the potential to bring about numerous positive changes in society, including enhanced productivity, improved healthcare, and increased access to education. AI-powered technologies can also help solve complex problems and make our daily lives easier and more convenient.

What makes a machine learning model successful? ›

To ensure that an ML project is worth the investment, three critical criteria must be taken into account: the ability to generate revenue, the ability to solve real-world problems, and the availability of resources.

What is the easiest way to explain machine learning? ›

Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It is a powerful and versatile tool that can solve complex problems and enhance human capabilities.

How can machine learning make our life better? ›

Moreover, Machine Learning can empower surgical robots to help doctors in medical procedures while ensuring minimal invasion and high precision. This achievement can improve the success rates of surgical procedures and accelerate turnaround time with cost benefits.

How does AI predict fashion trends? ›

Runway analysis

AI models today can look at how runway styles will permeate and influence luxury and mass market styles. They scan huge collections of images and infer information about popular patterns, cuts, color palettes, and so on.

How does H&M keep up with trends? ›

H&M is known for its fast fashion business model, which allows it to quickly adapt to changing fashion trends. The company has a very short design-to-production cycle, which means that new designs can be in stores within a few weeks of being created.

How can AI predict trends? ›

How does AI for trend analysis work? AI in trend analysis transforms the approach to identifying and predicting market trends using advanced data analytics, predictive modeling, and adaptive learning algorithms.

How does H&M forecast demand? ›

This is precisely where data science plays a vital role in helping H&M predict market demands. To achieve accurate predictions, H&M gathers data from various sources, including historical sales data, fashion trends, customer preferences, social media, and even external factors like weather.

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