批判性地思考数据
First, Hamish explains, clients must understand where waste is being generated from. 要做到这一点, 国际空间站 uses an AI-based kitchen waste management system that analyses food waste across its global portfolio of businesses. 国际空间站 collects data from thousands of 国际空间站 locations and through the millions of transactions that occur 每周.
“利用这些数据, 我们发现,生产辅料, plate waste and over production of main meals contribute to almost 60% of the total food waste generated in our business,哈米什解释道。. These insights have helped 国际空间站 work with local site teams to strategically plan menus and optimise food portions as well as set specific targets for each country. Reviewing data sets to understand the nuances of individual locations as part of wider global trends is essential, 哈米什补充道.
“It was important for us to be clear about how we were going to measure greenhouse gas emissions and food waste,他解释道. This included taking guidance from the World Resources Institute and Winnow, 商业食品垃圾解决方案, on how the company should be categorising waste and emissions across the total food lifecycle and communicating that across the global business.
Hamish explains that the frequency with which organisations review their data is important. “这是每天审查数据的纪律, 每周, monthly—and then putting in corrective actions at a site or micro-level—that has been the basis for our success.”
Technologies can help companies to gather information at different sites. An innovative way 国际空间站 has been able to leverage data is through sensor technology. This not only helps capture how many people go into the office each day but also how many eat at the on-site restaurant. “跟踪这些模式确实有助于我们最大限度地减少浪费.”
这项技术现在有95%的准确率, 这有助于网站通过提前计划来最大限度地减少浪费. 通过这些数据洞察, 国际空间站目前的集体年储蓄率为985,为顾客处理了1000吨食物垃圾. That is equivalent to saving 2,463,000 meals and reducing CO2 emissions by 4,200 tonnes.