Retailers: Leverage AI For A Greener Planet And Greener Financial Statements

Retailers: Leverage AI For A Greener Planet And Greener Financial Statements

Written by Mark Krupnik, CEO at Retalon. Read the original article published on Forbes here.


 

There are some who believe that sustainable supply chains that lead to a greener planet can only exist at the expense of the business. As if it were some sort of “can’t have the cake and eat it too” type of deal. This is a misconception.

In a 2018 report, Gartner, Inc. stated that “As organizations declare long-term environmental sustainability as a key priority, translating boardroom objectives into supply chain projects can be challenging.” While optimizing a retail supply chain for efficiency and reduction of waste requires careful planning and consideration, machine learning and predictive analytics technology have changed the rules of the game.

 

The Retail Sustainability Problem

The retail and consumer packaged goods (CPG) supply chain sector has been abruptly disrupted by the global pandemic. While retailers were already undergoing a digital transformation, worldwide Covid-19-related restrictions have greatly accelerated this evolution.

Consumers expect to seamlessly interact with brands and retailers across multiple channels, such as brick-and-mortar, social media and e-commerce. And retailers are rushing to offer new fulfillment options, like “buy online, pick up in-store” (BOPIS), curbside pickups, cashless checkouts, drop-shipping, mobile shopping and more.

While the shopping experience has never been easier, and successful retailers are dynamically operating across all these channels, there are serious environmental costs associated with this:

• Needless Production: Many retailers are stocking up significantly more inventory than they need to protect themselves from volatility in the marketplace.

• Increased Carbon Footprint: The shift to “next-day” e-commerce has increased the logistics associated with fulfilling an order and accepting returns.

• Elevated Levels Of Waste: The exponential growth of returns has not only cut into margins but also increased the amount of inventory that ends up in the trash because the retailer isn’t able to resell it.

• Outrageous Amounts Of Packaging: Since more orders are fragmented and sent separately, there is a huge amount of plastic waste an average consumer deals with.

According to a McKinsey report, which looked at the fashion industry specifically, researchers project that the “rise in volumes could push carbon emissions to around 2.7 billion metric tons a year by 2030.” This increase will have devastating effects on our planet.

 

How Machine Learning And Predictive Analytics Can Reduce The Carbon Footprint

Solving the challenges outlined above isn’t easy without jeopardizing a retailer’s agile operation. Producing less product may result in lost revenue. Refusing returns is likely to reduce e-commerce sales. Reducing fulfillment options decreases the level of customer service.

Retailers know they must prioritize environmental responsibility with minimal impact on the bottom line. This is something we help our clients accomplish through our retail technology, which leverages the power of machine learning (ML) and predictive analytics.

By better forecasting future demand and analyzing costs and opportunities much more granularly, ML and predictive analytics allow retailers to find that optimal balance in all areas of the supply chain. This includes materials and manufacturing, logistics and warehousing, and merchandising, fulfillment and returns.

When working with an advanced retail analytics tool, businesses should prioritize:

• Eliminating Needless Inventory: By leveraging better forecasts, retailers can reduce needless manufacturing, purchases and eventual markdowns.

• Optimizing Logistics Routing: Retailers should pay attention to their ML-powered recommendations to work toward significantly reducing the carbon footprint with fewer containers in the ocean and fewer trucks on the road.

• Fine-Tuning Order Fulfillment: By leveraging predictive analytics, retailers can proactively bring inventory to where it needs to be to fulfill all orders, using local stores as order fulfillment nodes.

• Reducing Waste Heading To Landfills: Retailers should bring returns to locations where they have a high probability to be sold again, dramatically reducing situations where products end up in landfills because they didn’t sell.

 

Retailers Can Have The Cake And Eat It Too

Not only can retailers benefit from contributing to a greener planter, but they are also likely to see greener results in their key performance indicators (KPIs). The results of the actions mentioned above may include reduced inventory costs, increased sales, healthier margins and greater automation.

Moreover, today’s consumer expects more than quality products from their favorite brands — many want to see these companies join them in standing together for a future they believe in. In fact, according to Morningstar, sustainable fund flows in the United States continued at a record pace, more than doubling from 2019 to 2020.

We have a wonderful future to look forward to if we all do our part. Prioritizing the reduction of retail’s carbon footprint through the use of technology will improve our lives and the planet. And those companies that recognize the early signals and get on board are likely to reap the rewards.

 

Mark Krupnik, PhD, is one of the world’s top experts in advanced analytics & AI for retailers. He is the CEO at Retalon, and a contributor to Forbes Technology Council.

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