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

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More than ever, consumer, manufacturing and retail brands are relying heavily on their technology to unlock value – with artificial intelligence (AI) The retail market alone is set to reach 31 billion USD by 2028. However, due to the overwhelming fragmentation that exists in the AI ​​ecosystem, businesses in the retail and CPG sectors are unable to drive the business impact they are seeking from their technology systems.

To address these silos directly, headquarters in Mumbai and San Francisco Fractional AI developed an end-to-end AI platform, named artificial intelligence, allowing for connected and automated decisions between supply and demand. By transforming the way decisions are made, Asper aims to unlock growth and transform organizations into intelligent, adaptive businesses.

Through its automated decision-making platform, Asper unifies demand planning, sales and distribution, inventory planning, and pricing and promotions. It works with data to not only make proactive decisions, but to make decisions that help clients reach their potential – from the end result to the optimization of their workflow.

“Business success today is defined by how quickly and seamlessly brands can make decisions,” said Mohit Agarwal, CEO of Asper. “Unfortunately, brands — especially in CPG — find their efforts constantly being undermined by connectionless technology that has hindered their success rather than empowered them. Without the interconnectedness, the promise of future AI technologies, since their debut decades ago, would still be far away, rather than right here, right now. Asper seeks to address these challenges by promoting coherence 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 more than 10% potential growth opportunities in financials and more than 50% in automation decision making process.

Vulnerability impedes efficiency and growth

The idea behind Asper dates back almost five years, when Agarwal and his team began noticing a disturbing trend gaining momentum in the consumer goods, retail and manufacturing industries. They found that key business decisions were made across functions and lacked tactical coherence at the most granular levels, resulting in missed revenue opportunities.

In spite of robotic process automation (RPA) Once a technology boom in the CPG world, he believes it has failed to deliver productivity improvements beyond simple tasks. And still only “humans” can make the right decisions considering the vast amount of information and signals available in real time. RPA has failed to improve productivity beyond simple tasks.

And even as companies turn to AI for this purpose, building AI capabilities from the ground up will take considerable time and investment, including setting up teams and process in data scienceengineering and design.

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

Essentially, what customers see is an AI system that breaks down decision-making barriers and evolves companies to build automated ecosystems, redefine human roles, and AI to function. with scale and precision.

Asper’s Bidirectional Platform

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

With demand forecasting and planning software, Asper aims to dramatically improve forecast accuracy at the most actionable level of granularity. It can not only promote automatic forecast refinements and refinements, but also promote collaborative consensus planning on risks and opportunities. The platform integrates itself on top of existing data and systems to deliver incremental financial growth through leading operations, inventory optimization, and automation.

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

  1. Prediction: Early warning of risks and opportunities​ with detailed multi-level and multi-dimensional visibility in real time.
  2. Quantify and allocate: Quantify and prioritize risks and opportunities with a deeper understanding of demand drivers.
  3. Proposal and collaboration: AI-led regulatory actions, self-learning, regulatory recommendations for consensus planning.
  4. Automation and Integration: Establish a cognitive workflow with long-tail automation and seamless integration with planning and execution systems.

On the other hand, the revenue management aspect is where most of the AI ​​comes in—especially for strategic and tactical decisions. It helps to identify real-time opportunities and reduce strategic price intervention time to weeks from months. It features AI-based calendar optimization and account-by-account recommendations to enable KAM to execute promotions that deliver 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% improvement in the ROI of the promotion. It is also said to cut customer engagement and negotiation time in half with comprehensive visibility into internal, customer, and consumer KPIs.

What does this mean for real-world performance? According to Agarwal, the platform can help solve four key problems:

  1. Revenue leak at the intersection of supply and demand: The company is bringing together the right data strategy, artificial intelligence, and automated decision-making capabilities to capture opportunities at the intersection of supply and demand at the most granular levels in real-time that are being exploited. lost due to functional silos, slow response and human dependence.
  2. Relying solely on humans for decision making is slow and inefficient: Asper is building AI to make machine-first recommendations but also designing the right tools and frameworks for human involvement and intervention, leading to process transformation.
  3. Current analytical models focus only on limited drivers/KPIs: Purpose-built AI models capture trends and signals from hundreds of internal and external signals/KPIs/KPIs, and identify the right drivers and relevant data, and shades for each category.
  4. Difficulty getting AI from testing to scale-out: Asper is building AI software to add value at scale at low cost.

For example, Asper implemented its demand planning AI platform with a $5 billion food processing company in the United States. The implementation is focused on driving accuracy and automated forecasting at scale. They are covering over 11,000 SKUs at the distribution center granularity.

“In the first year of our partnership, we have improved our forecast accuracy by more than 8% and are aiming to achieve an additional 5% accuracy by the end of the year. We’ve also enabled zero-touch forecasting automation without human intervention for over 40% of our portfolio, growing to 60% by the end of the year,” Agarwal told VentureBeat.

In addition to day-to-day efficiency and revenue optimization, Asper opens up additional flexibility for businesses to avoid getting stuck in the linear AI maturity curve. With Asper, businesses are free to tailor their AI journey and success by providing them with the ability to seamlessly transition in and out of their AI infrastructure underpinning key components. they need without the waiting associated with linear growth.

By 2022, Asper has tested its platform on 5-10 customers and claims to achieve improvements in accuracy of more than 10-15 points and autonomous forecasting of up to 60%. The company has built a multidisciplinary team for AI software innovation and development, bringing together leaders and talent in design, engineering, AI, and business consulting. By the end of the year, the company aims to achieve seven enterprise-wide deployments and 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 bring an impact of over $250 million to every customer using their platform. With AI at its core and substantial 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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