Leveraging big data analytics to sharpen sales and supply

Abundant levels of data created throughout the garment supply chain are increasingly being leveraged to boost sales and margins – and the more figures that are crunched the better.
By Sarah Gibbons

At each stage of the garment lifecycle – product concept, design, raw material purchase, manufacturing, transportation, sale, utilisation, after-sale service, and recycle/disposal – vast amounts of data is generated.

This provides a dataset “with big data characteristics,” according to researchers from Our Lady of the Lake University, School of Business and Leadership in San Antonio, Texas.

To improve the quality of this information, collectors should strive to capture data that is not readily available and develop algorithms for analytics that supply metrics better controlling product quality and safety. They should also focus data analytics on guiding timetables for predictive maintenance.

As a result, data analysis skills are an essential component of recruitment policy, according to KeunYoung Oh, chair and associate professor of the department of fashion and textile technology at State University of New York College.

“A good understanding of fashion fundamentals and data analytics should be considered as essential competencies by fashion brands and retailers when they are looking for new talents,” he said in an article earlier this year.

If businesses keep taking the same decisions in the same silos with the same data and logic then they will have missed an opportunity. Decisions that used to work in silos don't make sense anymore.” Michael Ross, DynamicAction

Broader adoption of big data and predictive analysis (BDPA) will steadily shift the focus from supply chains to demand chains, forecasts Michael Ross, co-founder and chief scientist at manufacturing data analysts DynamicAction.

Such work will indicate to businesses “how to maximise the inventory they’ve got,” ideally using artificial intelligence (AI) programmes on data collated from up to 50 sources including all levels of the supply chain, marketing, web analytics, price competitiveness data, logistics and inventory.

Armed with such demand data, supply chain decisions can be more efficient: “Data that is trusted, verifiable and managed well leads to trustworthy analysis and more justifiable decisions at every level of supply chain management,” according to a note from logistics solutions provider Purolator.

Handle with care

However Quantzig, a global data analytics and advisory firm, notes the complexity of fashion industry supply and distribution chains means data assessments need handling with care. They are “based on a delicate balance of different factors and problems such as changing trends, customers’ budget, and the absence of unified sizes.”

It argues that big data analytics can cope with such a challenge by using “data-driven sentiment analysis” solutions on social media and other platforms, such as product review channels, helping brands identify target markets by providing insights about customer preferences worldwide, analysing “product potential based on customer buying patterns” and returns history.

To achieve the goal of manufacturing products at lower cost, “and improve collaboration between supply chain, manufacturing, and sales operations, it’s crucial to rethink the production processes and bring more automation to factories,” the company says.

Quantzig’s manufacturing analytics solutions involve embedding industrial analytics into the decision-making and operational processes of a garment company, analysing activity across the supply chain, using predictive analysis to minimise equipment failures and unplanned downtime, detect product anomalies in production and gather real time production data to enable management to adjust operations.

DynamicAction’s Ross told just-style magazine his company analyses all available data sources to provide “a single view of the world.” He added: “Available data is the byproduct of consumer interaction and back office interaction combined with the cloud revolution.

“If businesses keep taking the same decisions in the same silos with the same data and logic then they will have missed an opportunity. Decisions that used to work in silos don't make sense anymore.”

Purolater says logistics data is a key element of this mix, helping managers make data-driven rather than instinctive decisions, claiming choices based on data analysis are easier to evaluate for key performance indicator targets, providing greater transparency to customers and suppliers.

“Sophisticated logistics data analysis incorporates multiple streams of data into a responsive system that facilitates communication between different links of the supply chain,” said a company note. It shared an example of how an AI system combining GPS with weather data highlighted localised bad weather that could delay a shipment, enabling relevant supply chain parties to make contingency arrangements, reducing the risk of reputational damage.

Personalised recommendations

Pure retail companies can also benefit of course. New York City-based fashion ecommerce business Rent the Runway rents designer garments, and relies heavily on data analytics.

It partners with designers to whom it refers customer feedback such as the most popular items, colours and styles, employing data analytics to provide customers with personalised recommendations for other potential garment rental options, including reviews from other customers

Supporting sustainability

Big data might also boost environmental and labour cost sustainability in fashion.

The Our Lady of the Lake University research said analytics can guide brands and manufacturers in saving resources, particularly where systems increase data-sharing security between supply chain actors – boosting trust.

Rameshwar Dubey, reader in operations management at John Moores University in the UK, and adjunct professor in supply chain management at Montpellier Business School in France, told just-style magazine big data analysis has “an immense role to play” in adding transparency to companies’ improvement of social and environmental performance, as well as economic statistics.

“The future lies in data, AI and analysis,” he said. “There is an incredible opportunity to improve social aspects of workers in the supply chain where, in the past, there has been too much vested interest to pursue improvements.”


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