Technology 2020: Trends In The Apparel Industry

The apparel industry is facing great changes. Technology trends offer interesting opportunities, if you know how to use them to improve your business. Big data, combined with production automation and product technology innovation, has the potential to make manufacturing more precise, as well as more local and sustainable. Potential benefits include higher speed, faster delivery times and lower cost than currently, as a result of reduced shipping times and lower stocks.

1. Big data

Big data refers to data sets so large that they can be difficult to manage using traditional data processing applications. These big data sets can generate business intelligence beyond unlocking hidden savings and fine-tuning production processes. By collecting the right data, businesses cannot only improve their economic development, but also their environmental and social performance.

The patterns and correlations that big data analytics reveal can benefit almost any industry, but in the supply chain big data is particularly interesting for the manufacturing industry. The information it generates can be used to make decisions, improve productivity and develop innovations.

Three promising ways in which big data can benefit the apparel industry are analysed below.

2. Big data for Corporate Social Responsibility (CSR)

Big data can help the apparel industry solve one of its main problems: unsold inventory. In March 2018, H&M reportedly had close to €3.8 billion worth of unsold clothes, mainly due to poor inventory management. When apparel is sourced through multiple supply chains, with multiple vendors in a non-standardised industry, this can create huge inventory and logistical problems that cost money and lead to an unnecessary waste of resources.

When you understand the entire process, from development to waste management, you are able to predict what products are actually needed to prevent overproduction and you can ship them when needed, reducing emissions from transportation. By making the supply chain more efficient, you are making it greener and more socially responsible. Other benefits of a more efficient supply chain include lower costs, reduced stock and a shorter time to market.

Big data applications for CSR may include:

  • up-to-date capacity planning;
  • predictive capacity planning;
  • predictive garment manufacturing;
  • predictive fabric manufacturing;
  • more control over supply and demand;
  • rating the CSR-performance of a company.

3. Big data for Growth

The right big data can be a great way to understand your customers’ behaviour. It can also provide insights into sales prospecting, business needs and product sales.

To keep production costs low, apparel companies can gather and analyse data to ensure they create clothes that their customers want to buy. Using customer buying habits data along with artificial intelligence and machine learning, companies are able to better predict styles and products that will sell in their target markets. This means they can leverage low-cost final inventory purchases to keep pricing so low that customers are more easily tempted to buy on impulse and buy more often.

Collecting data has become more common, especially among big fashion multinationals. Various types of resources to collect and manage valuable data already exist, such as Enterprise Resource Planning software and online analytics. Nonetheless, many fashion companies do not yet fully understand the potential of big data nor know how to use it to help their businesses grow.

Internet giant Amazon, for instance, is gaining ground in the fashion industry. This is an interesting development, since Amazon is not primarily driven by fashion knowledge, but by data and technology expertise. Amazon has a patented factory model for on-demand manufacturing of personalised garments with next-day delivery. This technology seems to be a good example of an application for big data in the apparel industry, combining predictive consumer behaviour monitoring with make-what-you-sell production and up-to-the-minute distribution.

Using big data for growth essentially means optimising your supply chain and unlocking your potential growth areas.

Big data insights can help your company grow in many ways, including:

  • finding new leads;
  • generating repeat sales;
  • increasing conversion rates;
  • predicting future sales;
  • reducing costs by optimising your supply chain;
  • communication — Enterprise Resource Planning software;
  • predictive selling, meaning shoppers receive products based on software predictions of their needs and wants.

4. Big data for online shopping and direct-to-consumer (D2C) sales

Direct sales can offer great opportunities in the global apparel market, where they are becoming a key retail channel. Not only do direct-to-consumer (DTC) sales allow companies to forge direct relationships with consumers, they also open up an opportunity to collect big data from those consumers. The intelligence this big data generates, in turn, enables businesses to improve their performance. This leads many large companies to invest in D2C sales. For example, Nike announced plans to expand its D2C sales by 250% by 2020.

Online sales platforms such as AliExpress, Alibaba, Amazon and eBay have been encouraging consumers to buy directly from factories in Asia, making it easy and cheap for both buyers and sellers. Alibaba, for instance, has recently announced a new warehouse in Belgium set to begin operating in 2021. These developments are all expected to boost D2C sales.

Big data benefits of online shopping and D2C sales include:

  • speed to market;
  • better customer relationship management;
  • better browser experience;
  • personalised shopping experience;
  • unified commerce and omnichannel strategies.

Many apparel producers from developing countries currently have limited or no access to valuable data. Often, the only available data are those shared by buyers. As you learn to use big data, you may become less dependent on intermediaries and gain more control over your own supply and demand.

Leave a Comment