At the beginning of the Covid-19 pandemic, many manufacturers found it hard to keep up with the changing times as most people began to change how they spend their money. While Procter & Gamble Co (NYSE: PG) has years of information on how the production lines within the company work, predictions completely changed in 2020. Both hand sanitizer and toilet paper became some of the hottest commodities, said the company’s chief analytics and data officer, Guy Pieri.
Peri talked to the CEO and founder of Ren.ai.ssance Insights, Jaime Fitzgibbon, during the VentureBeat’s conference and said that it’s something they’ve never seen before. The company planned to use AI to constructively disrupts how supply chain operations were conducted in 2021. However, that initial plan didn’t include the Covid-19 crisis. Instead, Procter & Gamble’s artificial intelligence was meant to rely on the information collected in the past to create new action plans, drive decision-making, and forecasting demand.
Importance of an effective data science team
However, then the Covid-19 crisis rendered the company’s historical data almost useless. Overnight, Procter & Gamble Co (NYSE: PG) had to change its strategy and rely on new options for maintaining its supply chain operations. It all started with Covid-19 response data, consumer demand forecasts by the government, and its raw material inventory.
Procter & Gamble has done an exceptional job at growing its data science team. The team Peri oversees is vital when it comes to helping the company manufacture its wide variety of consumer goods while ensuring the production of satisfactory goods as well as minimizing waste. Fieri further stressed the need for having top-notch data management professionals to ensure the company has reliable, quality data passing through the algorithms that provide actions and insights. He said that this was the way to be successful with AI.
This basically meant that you need to start small. For example, Peri’s data science team at Procter & Gamble has small setting program pilots to study outcomes and algorithms before scaling up.
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