Fractal Introduces an Interconnected AI Platform to Automate Decision Making for CPG, Manufacturing and Retail

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More than ever, retail, FMCG and manufacturing brands have to rely heavily on their technology to unlock value, with the retail artificial intelligence (AI) market alone set to hit $31 billion by 2028. However, due to the immense fragmentation that exists across the AI ​​ecosystem, companies in the retail and CPG categories are unable to generate the business impact they seek from their technology stacks.

To address these silos head-on, Mumbai and San Francisco-based Fractal AI developed an end-to-end AI platform, dubbed Asper AI, that enables interconnected and automated decisions between demand and supply. By changing the way decisions are made, Asper aims to unlock growth and transform organizations into adaptive intelligent enterprises.

Through its autonomous decision platform, Asper unifies demand planning, sales and distribution, inventory planning, and pricing and promotions. It works with data to not only deliver proactive decisions, but also to deliver decisions that help clients achieve their potential, from their bottom lines to optimizing their workflows.

“Today’s business success is defined by how quickly and fluidly brands can make decisions,” said Mohit Agarwal, CEO of Asper. “Unfortunately, brands, especially in CPG, see their efforts constantly undermined by disconnected technology that inhibits their success, not enhances it. Without interconnection, the promise of future AI technologies, since they debuted decades ago, would still be distant, rather than here and now. Asper seeks to solve these challenges by driving interconnection through its autonomous decision-making platform.


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Asper’s parent company, Fractal, focuses on the CPG, retail and manufacturing industries and has identified over 10% potential growth opportunities in financial performance and over 50% in decision automation.

The gaping hole hindering efficiency and growth

The idea behind Asper dates back almost five years, when Agarwal and his team began to notice a disturbing trend gaining momentum in the consumer goods, retail and manufacturing industry. They realized that key business decisions were made in functional silos and lacked tactical consistency at the most granular level, leading to missed revenue opportunities.

Although robotic process automation (RPA) has been a booming technology in the CPG world, he believes it has failed to improve productivity beyond simple tasks. And he still depends only on “humans” to make accurate decisions considering a large amount of information and signals available in real time. RPA has failed to improve productivity beyond simple tasks.

And even if companies were to turn to AI for this, building AI capabilities from scratch requires a lot of time and investment, including building teams and processes in data science, engineering, and design.

“Through our experience at Fractal solving these problems for Fortune 500 clients, we have the building blocks to solve these problems,” Agarwal said. “With Asper AI, we are bringing this experience together and investing on behalf of our clients to create for them the next generation of AI software platform focused on autonomous decisions in the enterprise.”

What customers can essentially see is an AI system breaking down decision silos and evolving businesses to build automated ecosystems, redefining the roles of humans and AI to operate with scale and precision.

Asper’s two-pronged platform

Asper’s current lineup of offerings includes two modules: Dynamic Demand AI, which is used for demand planning and forecasting, and Revenue Management, which is a pricing and promotions platform.

With demand planning and forecasting software, Asper aims to deliver a significant improvement in forecast accuracy at the most granular levels of action. Not only can it drive autonomous forecast completion and adjustment, but it also fosters consensual collaborative planning around risks and opportunities. The platform integrates on top of existing data and systems to deliver incremental financial growth through inventory automation and optimization.

The platform is designed to empower demand planners in their role through the following four user stories:

  1. Stay ahead of the curve: Early warning of risks and opportunities with granular, multi-level, multi-horizon visibility in real time.
  2. Quantify and attribute: Quantifying and prioritizing risks and opportunities with a deeper understanding of the drivers of demand.
  3. Recommend and Collaborate: AI-led self-learning prescriptive actions, adjustment recommendations for consensus planning.
  4. Automation and integration: Cognitive workflow setup with long-tail automation and seamless integration with planning and execution systems.

On the other hand, the revenue management side is where most of the AI ​​comes in, especially for strategic and tactical decisions. Helps identify opportunities in real time and reduces strategic price intervention time to weeks from months. It features AI-based calendar optimization and per-account recommendations to enable KAMs to run promotions that meet internal and retail KPIs. The platform can track and monitor revenue growth management (RGM violations, risks and opportunities).

The company claims the platform can deliver 2-3% financial growth and 15-20% improvements in ROI from promotions. It is also said to halve time for customer negotiations and alignment with holistic visibility into internal, customer, and consumer KPIs.

What does this mean in terms of performance in the real world? According to Agarwal, the platform can help address four key issues:

  1. Leakage of revenue at the intersection of supply and demand: The company is bringing together the right data strategy, AI and autonomous decision making to capture opportunities at the intersection of demand and supply at the most granular level in real time that are lost due to functional silos, slow response and human dependency.
  2. Total reliance on humans for decision making alone is slow and inefficient: Asper is building the AI ​​to create machine recommendations first, but is also designing the right tools and framework for human engagement and intervention, leading to process transformation.
  3. Current analytics models focus only on limited drivers/KPIs: AI models are specifically designed to capture trends and cues from hundreds of internal and external signals/KPIs and identify the right drivers and relevant, nuanced data for each category.
  4. It’s hard to take AI from experimentation to deployment at scale: Asper is building AI software to drive value at scale at a fraction of the cost.

For example, Asper has deployed its demand planning AI platform with a $5 billion food processing company in the US. The deployment focuses on driving accuracy and autonomous forecasting at scale. They cover more than 11,000 SKUs at the granularity of a distribution center.

“In the first year of the partnership, we achieved a more than 8% improvement in forecast accuracy and are targeting an additional 5% accuracy by the end of this year. We have also enabled contactless, zero-intervention forecasting automation for more than 40% of the portfolio growing to 60% by the end of this year,” Agarwal told VentureBeat.

Beyond day-to-day efficiency and revenue optimization, Asper also unlocks additional flexibility for companies to avoid getting stuck in a linear AI maturity curve. With Asper, companies are free to tailor their AI journeys and successes by giving them the ability to seamlessly jump in and out of their AI infrastructure to bolster the key components they need, without the lead times associated with linear development.

Through 2022, Asper has tested its platform on 5-10 clients and claims to achieve accuracy improvements of more than 10-15 points and up to 60% of autonomous forecasts. The company has built a multidisciplinary team to innovate and develop AI software, bringing together leadership and talent in design, engineering, AI, and business consulting. By the end of this year, the company aims to achieve seven deployments across the enterprise and 2X+ growth in revenue and ARR.

“Asper’s vision is to be the preferred growth AI platform for CPGR and manufacturing. The team aspires to generate an impact of more than $250 million for every client that uses their platform. With AI at its core and a significant investment from Fractal to create a best-in-class AI platform, Asper aims to spread its wings by raising external capital in the future,” said Agarwal.

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