Fractal unveils interconnected AI platform to automate decision-making for CPG, manufacturing and retail

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More than ever, consumer goods, manufacturing and retail brands are having to rely heavily on their technology to unlock value – with the artificial intelligence (AI) retail market alone set to hit $31 billion by 2028. However, because of the immense fragmentation that exists across the AI ecosystem, businesses in the retail and CPG categories are unable to drive the business impact they are looking for from their technology stacks. 

To tackle these silos head-on, Mumbai and New York-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. 

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Through its autonomic decisioning platform, Asper unifies demand planning, sales and distribution, inventory planning, and pricing and promotion. It works with data to not only provide proactive decisions, but to provide decisions that help customers reach their potential – from their bottom-line results to optimizing their workflows.

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“Business success today is defined by how quickly and seamlessly brands are able to make decisions,” said Mohit Agarwal, CEO of Asper. “Unfortunately, brands — especially in CPG — find their efforts constantly undermined by disconnected technology that inhibits their success, not empowers it. Without interconnectedness, the future AI technologies promise, since they debuted decades ago, would still be distant, instead of right here, right now. Asper looks to solve these challenges by driving interconnectedness through its autonomous decisioning platform.”  

Asper’s parent company, Fractal, is focused on CPG, retail and manufacturing industries and has identified 10%+ potential growth opportunities in financial performance and more than 50% in the automation of decision-making. 

The gaping hole that hinders efficiency and growth

The idea behind Asper dates back to nearly five years ago, when Agarwal and his team began noticing an unsettling trend gaining momentum in the consumer goods, retail and manufacturing industry. They noticed that key business decisions were made in functional silos and lacked tactical coherence at the most granular level, resulting in missed revenue opportunities. 

Even though robotic process automation (RPA) has been a booming technology in the CPG world, he believes that it failed to deliver productivity improvements beyond simple tasks. And it is still only upon “humans” to make precise decisions considering a lot of available information and signals in real time. RPA has failed to deliver productivity improvements beyond simple tasks. 

And even if companies were to turn to AI for this, building AI capabilities from the ground up takes significant time and investment, including setting up 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,” said Agarwal. “With Asper AI, we are bringing together this experience 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 essentially get to see is an AI system that breaks decision silos and evolves companies to build automated ecosystems, redefining the roles of human and AI to operate with scale and precision. 

Asper’s two-pronged platform 

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

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

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

  1. Anticipate: Early warning on risks and opportunities​ with granular multilevel and multihorizon visibility in real time.
  2. Quantify and attribute: Quantification and prioritization of risks and opportunities with a deeper understanding of demand drivers.
  3. Recommend and collaborate: AI-led, self-learning prescriptive actions, adjustments 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. It helps identify real-time opportunities and reduce time to strategic pricing intervention to weeks from months. It features AI-based calendar optimization and recommendations by account to enable KAMs to execute promotions that deliver on internal and retailer KPIs. The platform can track and monitor revenue growth management (RGM violations, risks and opportunities). 

The company claims that the platform can deliver 2-3% financial growth and 15-20% improvements in promotions ROIs. It is also said to reduce time for customer negotiations and alignment to half with a holistic visibility on internal, customer and consumer KPIs. 

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

  1. Revenue leakage at the intersection of demand and supply: 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 dependence.
  2. Full dependence on humans only for decision-making is slow and inefficient: Asper is building the AI to create machine-first recommendations but also designing the right tools and framework for human participation and intervention, leading to process transformation.
  3. Current analytics models are focused on limited drivers / KPIs only: The AI models are purpose-built to capture trends and signals from 100s of internal as well as external signals / KPIs and identify the right drivers and data relevant and nuanced for each category.
  4. Difficult to take AI from experimentation to scale implementation: Asper is building the AI software to drive value at scale at a fraction of the cost.

For example, Asper has implemented its demand-planning AI platform with a $5 billion food processing company in the U.S. The implementation is focused on driving accuracy and autonomous forecasting at scale. They are covering more than 11,000 SKUs at a distribution center granularity.

“In the year one of the partnership, we have delivered 8%+ improvement in the forecast accuracy and aim for additional 5% accuracy by the end of this year. We have also enabled touchless forecast automation without human intervention for more than 40% of the portfolio growing to 60% by end of this year,” Agarwal told VentureBeat. 

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

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

“Asper’s vision is to be the most preferred growth AI platform for CPGR and manufacturing. The team aspires to deliver $250MM+ impact for every customer using their platform. With AI at the core and significant investment from Fractal to create a best-in-class AI platform, Asper aims to expand its wings by raising external capital in the future,” said Agarwal.

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Sri Krishna