An organization’s environmental, social, and governance (ESG) score evaluates its performance on various sustainability metrics, including environmental impact, social responsibility, and corporate governance. In recent years, large-scale businesses have faced mounting pressure from consumers, investors, and regulators to improve their ESG scores. Mainstream coverage of the consequences of climate change and pollution has tipped the scales in favor of sustainable business models. Many investors now regard an organization’s ESG score as a strong indicator of its long-term viability and a critical factor in its growth (or failure).
ESG IS CONSIDERED PART OF THE SOCIETAL LICENSE TO OPERATE
Large-scale stakeholders in the Food & Beverage industry have a particular incentive to improve their ESG scores. McKinsey & Company found that “[food] products making ESG-related claims generated outsize growth,” indicating a more significant consumer preference for high-ESG Food & Beverage products than products from other industries. [1] Additionally, research suggests that improving ESG helps large-scale Food & Beverage businesses cut costs. A four-year study found “companies with high ESG scores, on average, experienced lower costs of capital compared to companies with poor ESG scores in both developed and emerging markets.” [2]
One of the world’s largest industries, Food & Beverage is also its most egregious polluter. Food production generates about 35 percent of global man-made greenhouse gas emissions, and beverage packaging accounts for hundreds of millions of tonnes of plastic in the oceans. [3, 4] Consumers, investors, and regulators are increasingly aware of this issue — and expect a solution.
In this blog, we'll explore some of the most promising applications of artificial intelligence for large-scale Food & Beverage businesses to improve their ESG scores: (i) optimizing operational efficiency, (ii) automating data collection, and (iii) reducing waste along the supply chain. [5]
66% OF COMPANIES ARE USING OR CONSIDERING AI TO ADVANCE THEIR SUSTAINABILITY GOALS
The Food & Beverage industry relies on a complex web of logistics and operations: production, procurement, processing, packaging, distribution, and retail. Operational inefficiencies can occur anywhere along the supply chain and create unnecessary waste. Every day, equipment breakdown leaves produce rotting on the vine, suboptimal fleet routing generates excessive emissions, and machinery malfunction creates deformed products designated for the landfill.
Predictive analytics, or using AI to predict future phenomena, can prevent operational efficiencies. For example, large-scale Food & Beverage businesses can use predictive analytics to identify the most fuel-efficient shipping routes, minimizing emissions. Alaska Airlines saved 480,000 gallons of fuel over the course of a sixth-month predictive routing pilot program. [6]
Large-scale Food & Beverage companies need to process large volumes of data to calculate their ESG metrics: a time-consuming process for people — but not for AI. “Smart” tools save time, reduce labor costs, and eliminate the risk of (human) errors in data collection and analysis. Starbucks, for example, has rolled out more than 4,000 AI-enabled espresso machines in an effort (called “Deep Brew”) to integrate AI-powered data-processing tools into its equipment. [7]
Fourteen percent of food is wasted between harvest and retail, according to the United Nations. [8] AI-enabled inventory management can help large-scale F&B businesses reduce waste and improve their ESG scores. With automated inventory management systems, businesses gain insight into their inventory levels, expiration dates, and ordering patterns, allowing them to reduce overstocking and spoilage. This not only helps to cut down on waste but can also result in significant cost savings. In addition, automated inventory management can reduce the need for manual labor, allowing employees to focus on more valuable tasks.
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