How Bad Weather Can be Good for the Fashion Supply Chain

Even with all the technological advances in Meteorology, predicting weather will never be an exact science. Bad weather (super storms, tornados, earthquakes, etc…) can cause damage to factories or resources that halts clothing production, while unseasonably hot or cold weather can alter the demand for a product. Therefore, preparation is always the best defense when strategizing about how to manage a fashion supply chain.

 

PLANNING FOR CONSUMER BEHAVIOR BASED ON WEATHER

Planning for consumer behavior based on weather can be tricky. And whether a retailers’ supply chain is international or domestic, seasonal weather should always be factored when planning for the upcoming fiscal year. According to Paul Walsh, global director of consumer weather strategy at IBM, “retailers need to start thinking about the weather up to a year in advance by analyzing how product categories react to weather conditions.”

A recent IBM survey of retail executives concluded that 99 out of 100 said they factor weather into their planning, but only a quarter do so months in advance. Most retailers only consider weather weeks before their inventory hits stores, which is not only a mistake but a missed opportunity to maximize profits.

In 2018, the UK had one of the hottest summers on record, but this past summer had historically milder temperatures. According to Plantalytics, the lack of long term planning for the following retail year resulted in the “demand for skirts and sandals were down 10% and 5% respectively this May compared to the year-ago period. It also estimates that a milder June caused swimwear demand to drop by over a fifth.”

 

HOW TO CALCULATE THE WEATHER

Though Walsh says retailers should start thinking about the weather up to a year in advance, basing sales expectations on the previous year is a common mistake brands make. It is essential to take a longer look back at historical weather data when planning supply chain decisions like production. Walsh breaks down the formula brands need to use in order to be the most effective and data-driven in their planning processes. Walsh explains, “weather over a long stretch regresses to the mean, working out an average temperature based on multiple years is more effective. The demand for winter coats is unlikely to be the same from one year to the next, but it is likely to be similar across five years.”

 

PARTNERING WITH THE RIGHT TECHNOLOGY PROVIDER

There is a right way and a wrong way to use weather data to manage any supply chain. But whether it’s the wrong way or the right way, it won’t matter if your supply chain isn’t fully digitized. Businesses that haven’t digitized their supply chain can’t react effectively to changes in weather –or any other signal for that matter. Digitizing a supply chain can seem overwhelming but with the right partnerships, it can be painless!

While most businesses do have systems and strategies for tracking potential disruptions and preemptively addressing them, the fashion supply chain remains vulnerable. Because most fashion supply chain processes are so archaic, collecting data analytics is challenging. Fortunately, Suuchi, Inc’s proprietary, cloud-based software, the Suuchi GRID, is the ultimate solution for collecting data analytics in the fashion supply chain.

Among its many features and functions, the elite software tracks products from design to delivery with real-time updates, end-to-end transparency, and supply chain traceability. The Suuchi GRIDS functionality to track products –coupled with the Suuchi Inc fashion industry network, is what makes it the ultimate tool to help brands collect the data they need to make informed decisions from historical data that becomes predictive analytics.

The Suuchi GRID is a simple software that solves complicated problems. And its easy integration into any system makes it the best solution for any retailer or brand to help forecast more than the weather, but their production needs.

 

Written by Alexis Washington

Learn more about how to implement a data-driven supply chain

 

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