Logility, which offers “prescriptive supply chain planning solutions,” is diving into large language model technology with the acquisition of Garvis, an AI-native demand forecasting firm.
The company announced on Thursday that it has signed a definitive agreement to acquire Garvis, a Software-as-a-Service startup that combines large language models such as ChatGPT with artificial intelligence to generate demand planning for supply chains.
“We are at the precipice of a significant transformation in supply chain planning with advancements in technology, generational shifts of planners, and the significant speed of market changes and disruptions,” says Allan Dow, president of Logility. “With an AI-driven approach at their core, Garvis revolutionized the way companies forecast demand in a very dynamic market. We’re bringing them into Logility’s portfolio to accelerate our shared vision to break the boundaries of traditional myopic supply chain planning solutions.”
“We are at the precipice of a significant transformation in supply chain planning with advancements in technology, generational shifts of planners, and the significant speed of market changes and disruptions.”
Garvis created DemandAI+, a proprietary demand-planning platform that leverages Generative AI and machine learning to that create a “more inclusive, and intuitive planning paradigm that quickly digitizes supply chain relationships and exposes that data to any stakeholder across the organization.”
Logility will embed DemandAI+ into its Digital Supply Chain platform.
According to Garvis, its technology “allows companies to understand how to react to changes as they happen – ultimately improving forecast accuracy and service levels, even for heavily promoted, highly seasonal, or intermittent products.”
“Our clients have realized epic results by using AI-driven algorithms and natural language interfaces to gain insights into the peaks and troughs of demand and quickly communicate that knowledge to the rest of the organization,” says Piet Buyck, CEO of Garvis. “Results have included a 70% savings of weekly planning time, 15% to 30% reduction in forecast error, and improved inventory management – all with ridiculously fast implementation times.”
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