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Expired Grocery Stock Is Filling Landfills and Oceans, but AI Is Emerging as a Hero in Helping Grocers Mitigate Plastic and Food Waste

According to a recent report, 70% of all litter in the sea is plastic. While consumers’ plastic consumption is a clear culprit, grocery stores are also adding to this predicament with inventory overstock and shrink. Coinciding with Earth Day 2019, Rubikloud, a global leader in retail artificial intelligence (AI), released an infographic to raise awareness of grocers’ food waste challenges.

When unsold grocery stock reaches its sell-by or expiration date, store managers must dispose of the waste. For example, in one year alone, grocers toss 29,000 packages of lunch meat which equates to an estimated 17,500 pounds of waste. While plastic only accounts for 3% of that weight, the amount of plastic from pre-packaged lunch meats can fill up almost 10 whales per year. Adding to the environmental impact, food waste that decomposes in landfills produces methane, which can lead to higher temperatures around the globe.

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Artificial Intelligence: Helping Grocers Be Better Environmental Stewards
Rubikloud is helping grocers minimize their environmental footprint with its Price and Promotion Manager solution. The machine learning technology models the drivers behind consumer demand and builds better forecasts and product allocations in the store, minimizing overstock and shrink that end up in landfills and oceans every year.

For example, in an effort to minimize the amount of food waste they were producing, one North American grocer turned to Rubikloud to help improve their forecast accuracy. Results were extremely positive and diverted 17,842 pounds of pre-packaged lunch meats from the landfill.

“Over the years, retailers have been challenged with the inefficiencies in their supply chains and promotional strategies, which lead to product overstock and shrink,” said Kerry Liu, co-founder and CEO of Rubikloud. “Fortunately, the power of AI and machine learning can reduce product waste from grocers by providing them with actionable insights and improved forecasting.”

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Rubikloud’s Price and Promotion Manager applies machine learning to reduce the complexities of the promotion planning process to yield more accurate forecasting and automate critical decisions. As consumers’ preferences change, Rubikloud’s solution continuously improves decisions for grocers to increase promotional effectiveness that drives incremental customer engagement. By utilizing Price and Promotion Manager, grocers can:

  • Assess data to improve store level forecasts and prevent overstock
  • Identify the optimal price and promotional scope to drive sales
  • Measure historical mass promotion activities to enhance promotion planning to increase in-store traffic

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